Abstract
This paper proposes a novel method, called fuzzy two-dimensional principal component analysis (F2DPCA), which combines the two-dimensional principal component analysis (2DPCA) and fuzzy set theory. 2DPCA preserve the total variance by maximizing the trace of feature variance, but 2DPCA cannot preserve local information due to pursuing maximal variance. So, the fuzzy two-dimensional principal component analysis (F2DPCA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution local information of original samples. Experimental results on ORL and Yale face databases show the effectiveness of the proposed method.
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