Locality similarity and dissimilarity preserving support vector machine

Support vector machines (SVMs) are well-known machine learning algorithms, however they may not effectively detect the intrinsic manifold structure of data and give a lower classification performance when learning from structured data sets. To mitigate the above deficiency, in this article, we propose a novel method termed as Locality similarity and dissimilarity preserving support vector machine (LSDPSVM). Compared to SVMs, LSDPSVM successfully inherits the characteristics of SVMs, moreover, it exploits the intrinsic manifold structure of data from both inter-class and intra-class to improve the classification accuracy. In our LSDPSVM a squared loss function is used to reduce the complexity of the model, and an algorithm based on concave-convex procedure method is used to solve the optimal problem. Experimental results on UCI benchmark datasets and Extend Yaleface datasets demonstrate LSDPSVM has better performance than other similar methods.