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Pattern Recognition with Slow Feature Analysis
Characteristic analysis nonlinear function the input data the function of learning
2015/7/31
Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that extract slowly varying signals out of the input data. In this paper we describe its application to pattern...
An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
slow feature analysis nonlinear blind source separation independent component analysis
2015/7/15
We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of ...
How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
Slow feature analysis feature extraction classifi cation regression pattern recognition training graphs nonlinear dimensionality reduction supervised learning high-dimensional data implicitly supervised image analysis
2015/7/10
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We propose an extension of slow feature analysis (SFA) for supervised dimensionality reduction called grap...