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Independent Component Analysis.

Seungjin Choi
- pp 435-459
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TLDR
The standardization of the IC model is talked about, and on the basis of n independent copies of x, the aim is to find an estimate of an unmixing matrix Γ such that Γx has independent components.
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The article was published on 2012-01-01 and is currently open access. It has received 2296 citations till now. The article focuses on the topics: Independent component analysis.

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Learning Topic Models - Going beyond SVD

TL;DR: In this article, the authors formally justify nonnegative matrix factorization (NMF) as a main tool in this context, which is an analog of SVD where all vectors are nonnegative.
Proceedings Article

A framework for the quantitative evaluation of disentangled representations

TL;DR: A framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available is proposed and three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis.
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Multivariate curve resolution: a review of advanced and tailored applications and challenges.

TL;DR: A critical review of the recently published works of multivariate curve resolution, dealing with improvements in preprocessing methods, multi-set data arrangements, tailored constraints, issues related to non-ideal noise structure and deviation to linearity are proposed.
Posted Content

Linear Dimensionality Reduction: Survey, Insights, and Generalizations

TL;DR: Linear dimensionality reduction methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted as discussed by the authors.
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Independent component analysis: recent advances.

TL;DR: An overview of some recent developments in the theory of independent component analysis is provided, including analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model.
References
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Book

Elements of information theory

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book

Matrix computations

Gene H. Golub
Book

Introduction to Statistical Pattern Recognition

TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI

An information-maximization approach to blind separation and blind deconvolution

TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI

Independent component analysis, a new concept?

Pierre Comon
- 01 Apr 1994 - 
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).