JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2012, Vol. 42 ›› Issue (6): 37-42.

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Matrix metric learning algorithm based on likelihood ratio test with matrix normal distribution

QIAN Qiang, CHEN Song-can*   

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2012-11-02 Online:2012-12-20 Published:2012-11-02

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Abstract: Most metric learning algorithms involve tedious optimization procedure. In order to solve this problem, a metric for matrix data by using likelihood ratio test was defined based on the KISS algorithm (keep it simple and stupid). By introducing the matrix normal distribution into the likelihood ratio test, the proposed metric does not need to transform matrix pattern into vector pattern. The results showed that this algorithm could avoid the curse of dimension, could be more robust than KISS, and would not need to compute the inverse and eigen-decomposition of high dimensional matrix, which was faster than KISS. Experiments verified the advantages of the proposed algorithm.

Key words: matrix normal distribution, matrix metric, likelihood ratio test

CLC Number: 

  • TP391
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