Sparse Kernel Orthonormalized PLS for feature extraction in large data sets

@inproceedings{2006SparseKO,
  title={Sparse Kernel Orthonormalized PLS for feature extraction in large data sets},
  author={},
  year={2006}
}
  • Published 2006
In this paper we are presenting a novel multivariate analysis method for large scale problems. 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… CONTINUE READING
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