Results 191 to 200 of about 4,385,729 (229)

Least-Squares Independent Component Analysis

Neural Computation, 2011
Accurately evaluating statistical independence among random variables is a key element of independent component analysis (ICA). In this letter, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method.
Suzuki, T., Sugiyama, M.
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Independent Component Analysis

2021
Conventional EOFs yield orthogonal spatial patterns and uncorrelated time series. Non-correlation does not necessarily yield independence, which is a strong constraint compared to non-correlation. This chapter discusses the concept of independence, and its relation to non-normality and describes different ways to obtain independent components.
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Independent Component Analysis

2013
Imagine that you are attending a cocktail party, the surrounding is full of chatting and noise, and somebody is talking about you. In this case, your ears are particularly sensitive to this speaker. This is the cocktail-party problem, which can be solved by blind source separation (BSS).
Ke-Lin Du, M. N. S. Swamy
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Independent Components Analysis

2000
Independent Components Analysis has recently become an important tool for modelling and understanding empirical datasets. In this chapter we review the theoretical basis of ICA, outline an approach to non-stationary ICA, and describe a number of biomedical case studies.
Richard Everson, Stephen J. Roberts
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Independent Component Analysis

2011
Independent Component Analysis(ICA) is one of the methods for solving blind source separation in blind signal processing. This method seeks for a linear coordinate system to produce signals that are mutually statistically independent. Compared with Principal Component Analysis (PCA) based on correlation transform, ICA decorrelates signals and reduces ...
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Independent Component Analysis

Independent component analysis (ICA) was originally introduced in the signal processing literature as a blind source separation method with the goal to recover mutual independent non-Gaussian components based on an observed vector alone. The problem was first introduced in the 1980s and formalized in Comon ( 1994). From a statistical point of view, the
Nordhausen Klaus, Taskinen Sara
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Probabilistic Sequential Independent Components Analysis

IEEE Transactions on Neural Networks, 2004
Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data.
Max, Welling, Richard S, Zemel, Geoffrey E, Hinton   +2 more
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Independent component analysis: an introduction

Trends in Cognitive Sciences, 2002
Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals.
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Addressing distress management challenges: Recommendations from the consensus panel of the American Psychosocial Oncology Society and the Association of Oncology Social Work

Ca-A Cancer Journal for Clinicians, 2021
Teresa L Deshields, Ryan D Nipp
exaly  

Female erectile tissues and sexual dysfunction after pelvic radiotherapy: A scoping review

Ca-A Cancer Journal for Clinicians, 2022
Deborah C Marshall, Mas, Julie Bloom Bs, Mylin A Torres   +2 more
exaly