JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (1): 9-12.doi: 10.6040/j.issn.1672-3961.2.2014.091

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An improved method of covariance discriminative learning

REN Jieyi, WU Xiaojun   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2015-05-23 Revised:2014-12-11 Online:2015-02-20 Published:2015-05-23

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Abstract: Based on the original Covariance Discriminative Learning method, a bidirectional dimension reduction was applied on those covariance matrices, which lied on a Riemannian manifold. Combining these covariance matrices with an efficient Riemannian metric, i.e., log euclidean distance (LED), a kernel function that mapped the covariance matrix from Riemannian manifold to Euclidean space for classification was derived. As a result of dimension reduction of covariance matrices, this method improved accuracy of classification and reduced the complexity of computation. The results were observed through experiments on standard datasets.

Key words: discriminant analysis, riemannian metric, face recognition, covariance matrix, object classification

CLC Number: 

  • TP391.4
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