Semi-Supervised Local Structured Feature Learning with Dynamic Maximum Entropy Graph

Abstract

In this paper, we propose a novel semi-supervised dimensionality reduction method based on local structured feature learning with dynamic maximum entropy graph. The proposed method first learns a local discriminative embedding subspace from labeled data for preserving the intrinsic sub-manifold structure in each class by virtue of dynamic maximum entropy graph technique. Then, another auto-optimized k-nearest neighbor graph is constructed at learned embedded subspace to smooth the manifold of all labeled and unlabeled data such that each labeled sample and its neighbored unlabeled samples can be clustered into a same sub-manifold and possess the same label information. Most importantly, in order to guarantee an overall optimum, subspace learning and local structure graph optimizing are performed simultaneously rather than treat them as two irrelevant steps as done in most of graph-based semi-supervised learning methods, which can avoid the negative effects brought by noisy and redundant features. Experimental results on several real-world benchmarks demonstrate the superiorities of our method on sub-manifold structure exploration and classification task.