Independent Slow Feature Analysis and Nonlinear Blind Source Separation

DOI10.1162/neco.2007.19.4.994zbMath1154.94336OpenAlexW2156141384MaRDI QIDQ3440425

Tobias Blaschke, Laurenz Wiskott, Tiziano Zito

Publication date: 22 May 2007

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

Full work available at URL: https://doi.org/10.1162/neco.2007.19.4.994

Mathematics Subject Classification ID

General nonlinear regression (62J02) Detection theory in information and communication theory (94A13)

Related Items (3)

An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network ⋮ A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction ⋮ Improved graph-based SFA: information preservation complements the slowness principle

Cites Work

• Linear and nonlinear ICA based on mutual information-the MISEP method
• Information-theoretic approach to blind separation of sources in non-linear mixture
• Independent component analysis, a new concept?
• What Is the Relation Between Slow Feature Analysis and Independent Component Analysis?
• Indeterminacy and identifiability of blind identification
• Slow Feature Analysis: Unsupervised Learning of Invariances
• 10.1162/jmlr.2003.4.7-8.1319
• Kernel-Based Nonlinear Blind Source Separation
• Jacobi Angles for Simultaneous Diagonalization
• A generic framework for blind source separation in structured nonlinear models
• CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization

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