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Principal components: A descent algorithm
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DOI：10.1016/j.jcp.2014.02.033.png)
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Abstract
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A descent procedure is proposed for the search of low-dimensional subspaces of a high-dimensional space that satisfy an optimality criterion. Specifically, the procedure is applied to finding the subspace spanned by the first m singular components of an n-dimensional dataset. The procedure minimizes the associated cost function through a series of orthogonal transformations, each represented economically as the exponential of a skew-symmetric matrix drawn from a low-dimensional space. (C) 2014 Elsevier Inc. All rights reserved.
Keywords:
Principal component analysis
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