Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

DOI QR코드

DOI QR Code

Park, Chong-sun

  • 발행 : 2004.04.01

초록

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

키워드

Probabilistic Principal Component Analysis;Dimension Reduction;Latent Variable Model;Missing Values

참고문헌

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