2006
NeurIPS
NeurIPS 2006
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets
Abstract
In this paper we are presenting a novel multivariate analysis method. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method for feature extraction of labelled data.
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Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Feature Learning
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Optimization & Theory > Sparse Optimization
Machine Learning > Core Methods > Feature Extraction