|Økt samvirke og beslutningsstøtte – Case Salten Brann IKS Technical Report |
2017, (Pre SFI).
|Exploring the semantic gap for movie recommendations Conference |
Mehdi Elahi; Yashar Deldjoo; Farshad Bakhshandegan Moghaddam; Leonardo Cella; Stefano Cerada; Paolo Cremonesi
Proceedings of the Eleventh ACM Conference on Recommender Systems, Association for Computing Machinery New York, 2017, (Pre SFI).
In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it.
In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd.
To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations.
|Multimodal Retrieval with Diversification and Relevance Feedback for Tourist Attraction Images Journal Article |
Duc-Tien Dang Nguyen; Luca Piras; Giorgio Giacinto; G. Boato; Francesco G. B. DE Natale
In: vol. 14, no. 4, pp. 1-24, 2017, (Pre SFI).
In this article, we present a novel framework that can produce a visual description of a tourist attraction by choosing the most diverse pictures from community-contributed datasets, which describe different details of the queried location. The main strength of the proposed approach is its flexibility that permits us to filter out non-relevant images and to obtain a reliable set of diverse and relevant images by first clustering similar images according to their textual descriptions and their visual content and then extracting images from different clusters according to a measure of the user’s credibility. Clustering is based on a two-step process, where textual descriptions are used first and the clusters are then refined according to the visual features. The degree of diversification can be further increased by exploiting users’ judgments on the results produced by the proposed algorithm through a novel approach, where users not only provide a relevance feedback but also a diversity feedback. Experimental results performed on the MediaEval 2015 “Retrieving Diverse Social Images” dataset show that the proposed framework can achieve very good performance both in the case of automatic retrieval of diverse images and in the case of the exploitation of the users’ feedback. The effectiveness of the proposed approach has been also confirmed by a small case study involving a number of real users.
|Exploiting Food Choice Biases for Healthier Recipe Recommendation Conference |
David Elsweiler; Christoph Trattner; Morgan Harvey
ACM SIGIR Conference 2017, (Pre SFI).
By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have dierent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.
|Automatic identification of unknown names with specific roles Journal Article |
Samia Touileb; Truls Pedersen; Helle Sjøvaag
In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 150-158, 2017, (Pre SFI).
Automatically identifying persons in a particular role within a large corpus can be a difficult task, especially if you don’t know who you are actually looking for. Resources compiling names of persons can be available, but no exhaustive lists exist. However, such lists usually contain known names that are “visible” in the national public sphere, and tend to ignore the marginal and international ones. In this article we propose a method for automatically generating suggestions of names found in a corpus of Norwegian news articles, and which “naturally” belong to a given initial list of members, and that were not known (compiled in a list) beforehand. The approach is based, in part, on the assumption that surface level syntactic features reveal parts of the underlying semantic content and can help uncover the structure of the language.
|Visually-Aware Video Recommendation in the Cold Start Conference |
Mehdi Elahi; Reza Hosseini; Mohammad H. Rimaz; Farshad B. Moghaddam; Christoph Trattner
Proccedings of theACM Hypertext 2020 2017, (Pre SFI).
Tags: Video Recommendation| |
Recommender Systems (RSs) have become essential tools in any
video-sharing platforms (such as YouTube) by generating video
suggestions for users. Although, RSs have been e!ective, however,
they su!er from the so-called New Item problem. New item problem,
as part of Cold Start problem, happens when a new item is added to
the system catalogue and the RS has no or little data available for
that new item. In such a case, the system may fail to meaningfully
recommend the new item to any user.
In this paper, we propose a novel recommendation technique
based on visual tags, i.e., tags that are automatically annotated
to videos based on visual description of videos. Such visual tags
can be used in an extreme cold start situation, where neither any
rating, nor any tag is available for the new video item. The visual
tags could also be used in the moderate cold start situation when
the new video item has been annotated with few tags. This type
of content features can be extracted automatically without any
human involvement and have been shown to be very e!ective in
representing the video content.
We have used a large dataset of videos and shown that automatically
extracted visual tags can be incorporated into the cold start
recommendation process and achieve superior results compared to
the recommendation based on human-annotated tags.
| Word vectors, reuse, and replicability: Towards a community repository of large-text resources Proceeding |
M Fares; A Kutuzov; S Oepen; Erik Velldal
2017, (Pre SFI).
|Timing - small step for developers, giant leap for the media industry, IBC 2016 Conference |
Njål Borch; François Daoust; Ingar Mæhlum Arntzen
2017, (Pre SFI).
|Modeling event importance for ranking daily news events Conference |
Vinay Setty; Abhijit Anand; Arunav Mishra; Avishek Anand
Proceedings of the tenth acm international conference on web search and data mining, Association for Computing Machinery New York, 2017, (Pre SFI).
We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.
To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.
|Business models for academic prototypes: A new approach to media innovation Journal Article |
Lars Nyre; Joao Ribeiro; Bjørnar Tessem
In: he Journal of Media Innovations, vol. 4, no. 2, pp. 4-19, 2017, (Pre SFI).
This article introduces the concept of academic prototypes, and shows how they can lead to technological innovation in journalism. We propose an innovation method that transforms a value-oriented academic prototype into a market-oriented journalistic service. The principles for product development presented here are based on the lean startup method as well as business model canvassing. A prototype scenario shows how the locative information app PediaCloud could be transformed into a locative news service for a regional newspaper in Western Norway. Ideally, the academic prototype will be transformed into a novel and engaging way of reading news stories, and a profitable solution for the newspaper. Realistically, the team will have acquired empirical validation of the business model's strong and weak points. In the conclusion, we summarize the utility of the approach for validated learning, and make recommendations for further research on innovation with academic prototypes.
|The changing ecology of tools for live news reporting Journal Article |
Frode Guribye; Lars Nyre
In: Journalism Practice, vol. 10, no. 11, pp. 1216-1230, 2016, ISSN: 1751-2794, (Pre SFI).
Broadcast news channels provide fresh, continuously updated coverage of events, in sharp competition with other news channels in the same market. The live moment is a valuable feature, and broadcasters have always relied on teams that can react quickly to breaking news and report live from the scene. Technology plays an important role in the production of live news, and a number of tools are applied by skilled actors in what can be called an ecology of tools for live news reporting. This study explores new video tools for television news, and the tinkering conducted by the reporting teams to adapt to such tools. Six journalists and photographers at broadcaster TV 2 in Norway were interviewed about their everyday work practices out in the field, and we present the findings in an analysis where six aspects of contemporary live news reporting are explored: (1) from heavy to light equipment, (2) more live news at TV 2, (3) the practice of going live, (4) the mobility of live reporters, (5) tinkering to go live, and (6) quicker pace of production. In the concluding remarks we summarize our insights about live news reporting.
|The Future of Journalism as a System, Profession and Culture: The Perception of Journalism Students Journal Article |
Ana Milojevic; Aleksandra Krstić; Aleksandra Ugrinić
In: Media Research, vol. 22, no. 2, pp. 83-105, 2016, (Pre SFI).
Currently, there is clear need for traditional journalism to redeﬁ ne itself. The intention of this article is to portray the voices of future journalists in this quest. Therefore, Belgrade University journalism students were assigned to write down their contemplations about the journalism of tomorrow in essayistic form. In order to systematize their narratives, three theoretical understandings of jour-nalism are introduced based on a literature review: journalism as a societal system, profession and culture. The essays were analyzed using quantitative and qualitative content and critical discourse analyses. The students’ anticipated changes in journalism understood as a system, profession and culture are dis-cussed, with a special focus on language, in order to deconstruct how students evaluate the future of journalism. Furthermore, the article shows how students perceive their role in redeﬁ ning journalism.
|Samling av makt og myndighet Journal Article |
In: Stat & Styring , vol. 26, no. 4, pp. 32-35, 2016, (Pre SFI).
Evnen til å utnytte IKT spiller en avgjørende rolle for verdiskapningen og velferden i Norge. Det er nå på tide å samle makt og myndighet i en kraftfull enhet, for å unngå den ødeleggende kompetansestriden mellom etater som nå finner sted.
|Recommender Systems - Beyond Matrix Completion Journal Article |
Dietmar Jannach; Alexander Tuzhilin; Markus Zanker
In: Communications of the ACM, vol. 59, no. 11, pp. 94-102, 2016, (Pre SFI).
Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this multidis-ciplinary field and looked at recent efforts in the research community to consider the variety of factors that may influence the long-term success of a recommender system. The list of open issues and success factors is still far from complete and new challenges arise constantly that require further research. For example, the huge amounts of user data and preference signals that become available through the Social Web and the Internet of Things not only leads to technical challenges such as scalability, but also to societal questions concerning user privacy. Based on our reflections on the developments in the field, we finally emphasize the need for a more holistic research approach that combines the insights of different disciplines. We urge that research focuses even more on practical problems that matter and are truly suited to increase the utility of recommendations from the viewpoint of the users.
|Må vi finne opp hjulet på nytt? Online |
2016, (Pre SFI).
Devices and methods for power consumption control in powerline communications systems and apparatus Patent
Lydi Smaini; Alexandre Rouxel
2016, (Pre SFI).
The present disclosure includes systems and techniques relating to power line communications (PLC) systems and apparatus. In some implementations, a method includes determining information regarding a potential data rate to be used with a powerline communications (PLC) channel, reducing a bias current or voltage of an analog front end of a PLC transceiver based on the determined information to reduce power consumption of the analog front end of the PLC transceiver, and transmitting or receiving data over the PLC channel with the reduced bias current or voltage of the analog front end of the PLC transceiver.
|A survey of active learning in collaborative filtering recommender systems Journal Article |
Mehdi Elahi; Francesco Ricci; Neil Rubens
In: Computer Science Review, vol. 20, pp. 29-50, 2016, (Pre SFI).
In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.
|The enrichment of lexical resources through incremental parsebanking Journal Article |
V Rosén; M Thunes; P Haugereid; GS Losnegaard; H Dyvik; P Meurer; G Lyse; Koenraad De Smedt
In: 2016, (Pre SFI).
|Alleviating the new user problem in collaborative filtering by exploiting personality information Journal Article |
Ignacio Fernandez Tobias; Matthias Braunhofer; Mehdi Elahi; Francesco Ricci; Ivan Cantador
In: User Modeling and User-Adapted Interaction, vol. 26, no. 2-3, pp. 221-255, 2016, (Pre SFI).
The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.
|NorGramBank: A 'Deep' Treebank for Norwegian.Proceedings of LREC Proceeding |
H Dyvik; P Meurer; V Rosén; Koenraad De Smedt; P Haugereid; GS Losnegaard; G Lyse; M Thunes
2016, (Pre SFI).
Ingar Mæhlum Arntzen; Njål Borch
2016, (Pre SFI).
|Hapticolor: Interpolating color information as haptic feedback to assist the colorblind Proceeding |
M.G Carcedo; S.H Chua; S Perrault; P Wozniak; R Joshi; M Obaid; Morten Fjeld; S Zhao
2016, (Pre SFI).
| RAMPARTS: Supporting sensemaking with spatially-aware mobile interactions Journal Article |
P Wozniak; N. Goyal; P. Kucharski; L. Lischke; S. Mayer; Morten Fjeld
In: 2016, (Pre SFI).
|MWEs in Treebanks: From Survey to Guidelines Proceeding |
V Rosén; Koenraad De Smedt; GS Losnegaard; E Bejcek; A Savary; P Osenova
2016, (Pre SFI).
| Universal dependencies for Norwegian Proceeding |
Lilja Øvrelid; P Hohle
2016, (Pre SFI).
|“Practicing Audience-Centred Journalism Research.” Book Chapter |
Irene Costera Meijer
In: Witschge, T.; Anderson, C. W.; Domingo, D.; Hermida, A. (Ed.): Chapter 36, pp. 546-561, Sage, 55 City Road, London, 2016, ISBN: 9781473906532, (Pre SFI).
The production and consumption of news in the digital era is blurring the boundaries between professionals, citizens and activists. Actors producing information are multiplying, but still media companies hold central position. Journalism research faces important challenges to capture, examine, and understand the current news environment. The SAGE Handbook of Digital Journalism starts from the pressing need for a thorough and bold debate to redefine the assumptions of research in the changing field of journalism. The 38 chapters, written by a team of global experts, are organised into four key areas: Section A: Changing Contexts Section B: News Practices in the Digital Era Section C: Conceptualizations of Journalism Section D: Research Strategies By addressing both institutional and non-institutional news production and providing ample attention to the question 'who is a journalist?' and the changing practices of news audiences in the digital era, this Handbook shapes the field and defines the roadmap for the research challenges that scholars will face in the coming decades.
|VizRec: Recommending Personalized Visualizations Journal Article |
Belgin Mutlu; Eduardo Veas; Christoph Trattner
In: ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 6, no. 4, pp. 1-40, 2016, (Pre SFI).
Visualizations have a distinctive advantage when dealing with the information overload problem: since they
are grounded in basic visual cognition, many people understand them. However, creating the appropriate
representation requires specific expertise of the domain and underlying data. Our quest in this paper is to
study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has
to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a
visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects
of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can
we capture and use those aspects to recommend visualizations? This paper investigates strategies to
recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to
estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts
are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines
information on what a chart is about (tags) and how good it is (ratings). We present the design principles
behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a
crowd sourced study, and the analysis of strategies for visualization recommendation.
|Mediasync Report 2015: Evaluating timed playback of HTML5 Media Journal Article |
Njål Borch; Ingar Mæhlum Arntzen
In: Norut, 2015, ISBN: 978-82-7492-319-5, (Pre SFI).
In this report we provide an extensive analysis of timing aspects of HTML5 Media, across a variety of browsers,
operating systems and media formats. Particularly we investigate how playback compares to the progression of
the local clock and how players respond to time-shifting and adjustments in playback-rate.
Additionally, we use the MediaSync JS library to enforce correctly timed playback for HTML5 media, and indicate
the effects this has on user experience. MediaSync is developed based on results from the above analysis.
MediaSync aims to provide a best effort solution that works across a variety of media formats, operating systems
and browser types, and does not make optimizations for specific permutations..
|Active learning in recommender systems Book Chapter |
Neil Rubens; Mehdi Elahi; Masashi Sugiyama; Dain Kaplan
In: Ricci, Francesco; Rokach, Lior; Shapira, Bracha (Ed.): pp. 809-846, Springer, 2015, ISBN: 978-1-4899-7637-6, (Pre SFI).
In Recommender Systems (RS), a user’s preferences are expressed in terms of rated items, where incorporating each rating may improve the RS’s predictive accuracy. In addition to a user rating items at-will (a passive process), RSs may also actively elicit the user to rate items, a process known as Active Learning (AL). However, the number of interactions between the RS and the user is still limited. One aim of AL is therefore the selection of items whose ratings are likely to provide the most information about the user’s preferences. In this chapter, we provide an overview of AL within RSs, discuss general objectives and considerations, and then summarize a variety of methods commonly employed. AL methods are categorized based on our interpretation of their primary motivation/goal, and then sub-classified into two commonly classified types, instance-based and model-based, for easier comprehension. We conclude the chapter by outlining ways in which AL methods could be evaluated, and provide a brief summary of methods performance.
|Checking, sharing, clicking and linking: Changing patterns of news use between 2004 and 2014. Journal Article |
Irene Costera Meijer; Tim Groot Kormelink
In: Digital Journalism, vol. 3, no. 5, pp. 664-679, 2014, ISSN: 2167-0811, (Pre SFI).
This paper challenges the generally taken-for-granted automatic link between media platforms, media technology and news user practices. It explores what has changed in people’s news consumption by comparing patterns in news use between 2004–2005 and 2011–2014. While new, social and mobile media technologies did not unleash a revolution in people’s dealings with news, they have facilitated, deepened and broadened user practices we already found in 2004–2005: monitoring, checking, snacking, scanning, watching, viewing, reading, listening, searching and clicking. In addition, these forms of news usage appear to increasingly order, control, organize and anchor other practices and the experience of time and environment in which they occur. Meanwhile, new and mobile news practices like linking, sharing, liking, recommending, commenting and voting have not become as central to news consumption as often assumed.
|Method for processing a signal using an approximate map algorithm and corresponding uses Patent |
2013, (Pre SFI).
Tags: Map algorithm| |
The invention concerns a method for processing a signal using an approximate MAP (maximum a posteriori) algorithm for determining a likelihood ratio Λk X of a set of states X of a lattice at a time k, with each of said states being associated at least one intermediate variable belonging to a group comprising a so-called forward variable and a so-called backward variable, propagated by said MAP algorithm and recursively calculated respectively in a direct orientation and in an indirect orientation at said time k relative to said lattice. The invention is characterized in that said process comprises a step which consists in reducing the number of selected states by said MAP algorithm so as to calculate said likelihood ratio, and, for at least some unselected states, in assigning to said forward variable and/or said backward variable at least one specific value, to calculate an approximate likelihood ratio.
|Representing and resolving negation for sentiment analysis Proceeding |
E Lapponi; J Read; Lilja Øvrelid
2012, (Pre SFI).
|Method for processing a signal using an approximate map algorithm Patent |
2012, (Pre SFI).
Tags: Algorithm| |
A technique for receiving a data stream including a spreading sequence packet of information containing a data payload and, in addition to the data payload, packet overhead including at least periodic information and at least one unique section of known coded information that defines a unique position within the packet, includes performing a plurality of processing steps to detect the position of the unique section within the packet of information. The steps include detecting the periodicity of the periodic information in a first processing step; in a second processing step after periodicity in the received data stream has been determined, estimating the position of the unique section within the packet of information; and in a third processing step, correlating the information in the packet of information about the estimated position with the known coded information.
|Speculation and negation: Rules, rankers, and the role of syntax Journal Article |
Erik Velldal; Lilja Øvrelid; J Read; S Oepen
In: 2012, (Pre SFI).
|Pilot-aided channel estimation for OFDM/OQAM Conference |
Jean-Philippe Javaudin; D. Lacroix; Alexandre Rouxel
vol. 3, The 57th IEEE Semiannual Vehicular Technology Conference 2003, (Pre SFI).
Tags: OFDM| |
OFDM/offsetQAM is an interesting alternative to classical OFDM modulation, as it does not require the use of guard interval. This characteristic makes its spectral efficiency optimal. On the other hand, this modulation is less robust to Rayleigh fading channel. Indeed, when classical channel estimation used for OFDM modulation is applied straightforwardly to OFDM/OQAM modulation, an intrinsic inter-symbol-interference is observed. This deeply degrades its performances. In this paper, we theoretically explain this phenomenon and propose a reliable method to significantly reduce it. Results of different methods of pilot-aided channel estimation over delay-Doppler channels are shown in this paper.
|Unsupervised adaptive separation of impulse signals applied to EEG analysis Conference |
Alexandre Rouxel; Daniel Le Guennec; Odile Macchi
vol. 1, IEEE International Conference on Acoustics, Speech, and Signal Processing Turkey, 2000, (Pre SFI).
Tags: analysis| |
In this paper the theoretical properties of a novel self adaptive
source separation algorithm are studied. It is a normalized
version of a modified relative gradient. It is shown that its
stability domain in terms of the normalized kurtosises of
sources is complementary of the unmodified gradient
algorithm. So it can separate a source with a very high kurtosis
from other sources having positive kurtosis. The algorithm is
then used to analyze EEG signals because they often have
positive kurtosises especially for patients suffering from
epilepsy. The good behavior of this novel algorithm is
illustrated via simulated data and then demonstrated with real
signals in an EEG analysis to separate an epileptic source from
other brain signals
MORS 2021: 1st Workshop on Multi-Objective Recommender Systems. Proceeding
Himan Abdollahpouri; Mehdi Elahi; Masoud Mansoury; Shaghayegh Sahebi; Zahra Nazari; Allison Chaney; Babak Loni
Publications from 2020 and before are not direct results of the SFI MediaFutures, but are key results from our team members working on related topics in MediaFutures.