Literature DB >> 16907634

What is the relation between slow feature analysis and independent component analysis?

Tobias Blaschke1, Pietro Berkes, Laurenz Wiskott.   

Abstract

We present an analytical comparison between linear slow feature analysis and second-order independent component analysis, and show that in the case of one time delay, the two approaches are equivalent. We also consider the case of several time delays and discuss two possible extensions of slow feature analysis.

Mesh：

Year:  2006        PMID： 16907634     DOI： 10.1162/neco.2006.18.10.2495

Source DB:  PubMed          Journal:  Neural Comput        ISSN： 0899-7667            Impact factor:   2.026


  8 in total

1.  Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9.

Authors:  Christian R Schwantes; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2013-04-09       Impact factor: 6.006

2.  An application of slow feature analysis to the genetic sequences of coronaviruses and influenza viruses.

Authors:  Anastasios A Tsonis; Geli Wang; Lvyi Zhang; Wenxu Lu; Aristotle Kayafas; Katia Del Rio-Tsonis
Journal:  Hum Genomics       Date:  2021-05-07       Impact factor: 4.639

3.  Ideal Binocular Disparity Detectors Learned Using Independent Subspace Analysis on Binocular Natural Image Pairs.

Authors:  David W Hunter; Paul B Hibbard
Journal:  PLoS One       Date:  2016-03-16       Impact factor: 3.240

Review 4.  Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments.

Authors:  Soumendranath Bhakat
Journal:  RSC Adv       Date:  2021-03-17       Impact factor: 3.361

5.  Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis.

Authors:  Chang Li; Zhe Zhou; Chenglin Wen; Zuxin Li
Journal:  ACS Omega       Date:  2022-02-16

Review 6.  Collective variable discovery in the age of machine learning: reality, hype and everything in between.

Authors:  Soumendranath Bhakat
Journal:  RSC Adv       Date:  2022-09-02       Impact factor: 4.036

7.  Sustained firing of model central auditory neurons yields a discriminative spectro-temporal representation for natural sounds.

Authors:  Michael A Carlin; Mounya Elhilali
Journal:  PLoS Comput Biol       Date:  2013-03-28       Impact factor: 4.475

8.  Modeling molecular kinetics with tICA and the kernel trick.

Authors:  Christian R Schwantes; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2015-02-10       Impact factor: 6.006
  8 in total

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