Fast deflation sparse principal component analysis via subspace projections

ABSTRACT The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Householder QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCA-SP, is developed for SPCA. This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and computational cost. Comparative experiments on the benchmark data sets confirm the effectiveness of the proposed method.

• Publication year

  2020

• Venue

  Journal of Statistical Computation and Simulation

• Publication date

  2020-02-16

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1080/00949655.2020.1728761
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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