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Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams.

Varun Raj Kompella¹, Matthew Luciw, Jürgen Schmidhuber

¹IDSIA, SUPSI, USI, Galleria 2, Manno-Lugano 6928, Switzerland. varun@idsia.ch

Neural Computation

|August 1, 2012

Summary

We developed incremental slow feature analysis (IncSFA), an efficient method for high-dimensional data. IncSFA offers linear complexity and biological plausibility, overcoming limitations of batch slow feature analysis (BSFA).

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Area of Science:

Machine Learning
Computational Neuroscience

Background:

Traditional batch slow feature analysis (BSFA) struggles with high-dimensional data, requiring complex architectures.
BSFA's cubic complexity limits its direct application to large input streams.

Purpose of the Study:

Introduce incremental slow feature analysis (IncSFA) for efficient feature extraction from high-dimensional data streams.
Enhance the tractability and biological plausibility of slow feature analysis.

Main Methods:

IncSFA combines candid covariance-free incremental principal components analysis (CCIPCA) and covariance-free incremental minor components analysis (CIMCA).
Feature updating complexity is linear with input dimensionality, avoiding covariance matrix computation.

Main Results:

IncSFA achieves linear complexity, significantly outperforming BSFA's cubic complexity.
IncSFA demonstrates comparable feature learning to BSFA and handles cases where BSFA fails.
IncSFA's updates exhibit Hebbian and anti-Hebbian forms, increasing biological plausibility.

Conclusions:

IncSFA provides a computationally efficient and scalable alternative to BSFA for high-dimensional data.
The method is suitable for real-time applications, such as in autonomous agents.
IncSFA extends the applicability and biological relevance of slow feature analysis.