Maximum Fuzzy Marginal Projection via Patch Alignment Framework
Xu Ji · 2016
Patch alignment(PA)framework provides us a useful way to obtain the explicit mapping for dimensionality reduction.Under the PA framework,we proposed a fuzzy maximum marginal projection for dimensionality reduction.In our paper,the fuzzy set theory was introduced in the design of new method.The similar neighbors obtained by the nonnegative least squares method were used to construct the similar membership degree matrix.Based on the similar membership degree matrix,we redefined the fuzzy weight and the fuzzy marginal patch means.The fuzzy weight can reduce the influence caused by the overlap and outliers to some extent.The fuzzy marginal patch means specify the contributions of each sample to the classification.Under the PA framework,the difference among intra-class samples caused by the variety of the illumination can be degraded.And the distances between different categories are enlarged in the transformed space.The experimental results on the UCI Wine,Yale and Yale-B databases demonstrate the effectiveness of the new methods,especially in dealing the changing illumination on images.