A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals

DOI10.1162/0899766053011474zbMath1092.93614OpenAlexW2097620723WikidataQ45966534 ScholiaQ45966534MaRDI QIDQ5696495

Yan Karklin, Michael S. Lewicki

Publication date: 18 October 2005

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

Full work available at URL: https://doi.org/10.1162/0899766053011474

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Stochastic learning and adaptive control (93E35)

Related Items (16)

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Uses Software

• Bubbles

Cites Work

• Second order nonstationary source separation
• Topographic Independent Component Analysis
• Slow Feature Analysis: Unsupervised Learning of Invariances
• Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video
• Blind separation of instantaneous mixtures of nonstationary sources
• Random cascades on wavelet trees and their use in analyzing and modeling natural images

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