TY - GEN
ID - cogprints8966
UR - http://cogprints.org/8966/
A1 - Escalante-B., Alberto-N.
A1 - Wiskott, Prof. Dr. Laurenz
TI - How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
Y1 - 2013/02//
N2 - 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 graph-based SFA (GSFA). The algorithm extracts a label-predictive low-dimensional set of features that can be post-processed by typical supervised algorithms to generate the ?nal label or class estimation. GSFA is trained with a so-called training graph, in which the vertices are the samples and the edges represent similarities of the corresponding labels. A new weighted SFA optimization problem is introduced, generalizing the notion of slowness from sequences of samples to such training graphs. We show that GSFA computes an optimal solution to this problem in the considered function space, and propose several types of training graphs. For classi?cation, the most straightforward graph yields features equivalent to those of (nonlinear) Fisher discriminant analysis. Emphasis is on regression, where four different graphs were evaluated experimentally with a subproblem of face detection on photographs. The method proposed is promising particularly when linear models are insufficient, as well as when feature selection is difficult.
AV - public
KW - Slow feature analysis
KW - feature extraction
KW - classi?cation
KW - regression
KW - pattern recognition
KW - training graphs
KW - nonlinear dimensionality reduction
KW - supervised learning
KW - high-dimensional data
KW - implicitly supervised
KW - image analysis.
ER -