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Titlebook: Independent Component Analysis and Signal Separation; 7th International Co Mike E. Davies,Christopher J. James,Mark D Plumble Conference pr

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書(shū)目名稱(chēng)Independent Component Analysis and Signal Separation
副標(biāo)題7th International Co
編輯Mike E. Davies,Christopher J. James,Mark D Plumble
視頻videohttp://file.papertrans.cn/464/463380/463380.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Independent Component Analysis and Signal Separation; 7th International Co Mike E. Davies,Christopher J. James,Mark D Plumble Conference pr
描述This volume contains the papers presented at the 7th International Conference on Independent Component Analysis (ICA) and Source Separation held in L- don, 9–12 September 2007, at Queen Mary, University of London. Independent Component Analysis and Signal Separation is one of the most exciting current areas of research in statistical signal processing and unsup- vised machine learning. The area has received attention from several research communities including machine learning, neural networks, statistical signal p- cessing and Bayesian modeling. Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series. This year’s event was organized by the EPSRC-funded UK ICA Research Network (www.icarn.org). There was also a minor change to the conference title this year with the exclusion of the word‘blind’. The motivation for this was the increasing number of interesting submissions using non-blind or semi-bli
出版日期Conference proceedings 2007
關(guān)鍵詞DOM; Estimator; Information; Minimum; Minimum Description Length; algorithms; audio segmentation; auditory
版次1
doihttps://doi.org/10.1007/978-3-540-74494-8
isbn_softcover978-3-540-74493-1
isbn_ebook978-3-540-74494-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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Blind Separation of Instantaneous Mixtures of Dependent Sourcestween the sources and explicitly consider that they are dependent. We introduce three particular models of dependent sources and show that their cumulants have interesting properties. Based on these properties, we investigate the behaviour of classical Blind Source Separation algorithms when applied
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Optimal Joint Diagonalization of Complex Symmetric Third-Order Tensors. Application to Separation ofves the joint diagonalization of a set of symmetric third-order tensors is proposed. The application to the separation of non-gaussian sources using fourth order cumulants is particularly investigated. Finally, computer simulations on synthetic signals show that this new algorithm improves the STOTD
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Using State Space Differential Geometry for Nonlinear Blind Source Separationime series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series that has a . density function equal to the product of density functions of individual components. In an earlier paper, it was shown that the phase space density fun
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Copula Component Analysis It differs from ICA which assumes independence of sources that the underlying components may be dependent by certain structure which is represented by Copula. By incorporating dependency structure, much accurate estimation can be made in principle in the case that the assumption of independence is
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Shifted Independent Component Analysista modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation. Here, we explore the fact that a shift . in the time domain corresponds to a multiplication of .. in the frequenc
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