What Is the Relation Between Slow Feature Analysis and Independent Component Analysis?

DOI10.1162/neco.2006.18.10.2495zbMath1107.68079OpenAlexW2166008640WikidataQ51935224 ScholiaQ51935224MaRDI QIDQ3417434

Tobias Blaschke, Pietro Berkes, Laurenz Wiskott

Publication date: 29 January 2007

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2006.18.10.2495

zbMATH Keywords

data analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Pattern recognition, speech recognition (68T10)

Related Items (5)

A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction ⋮ Predictive Coding and the Slowness Principle: An Information-Theoretic Approach ⋮ On Blind Separability Based on the Temporal Predictability Method ⋮ Independent Slow Feature Analysis and Nonlinear Blind Source Separation ⋮ A Maximum-Likelihood Interpretation for Slow Feature Analysis

Cites Work

• Second-order blind source separation in the Fourier space of data
• Independent component analysis, a new concept?
• Indeterminacy and identifiability of blind identification
• Slow Feature Analysis: Unsupervised Learning of Invariances
• Blind separation of mixture of independent sources through a quasi-maximum likelihood approach
• Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses
• Jacobi Angles for Simultaneous Diagonalization
• CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization

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