Joint Dynamic Manifold and Discriminant Information Learning for Feature Extraction

Neighborhood reconstruction is a good recipe to learn the local manifold structure. Representation-based discriminant analysis methods normally learn the reconstruction relationship between each sample and all the other samples. However, reconstruction graphs constructed in these methods have three limitations: 1) they cannot guarantee the local sparsity of reconstruction coefficients; 2) heterogeneous samples may own nonzero coefficients; and 3) they learn the manifold information prior to the process of dimensionality reduction. Due to the existence of noise and redundant features in the original space, the prelearned manifold structure may be inaccurate. Accordingly, the performance of dimensionality reduction would be affected. In this article, we propose a joint model to simultaneously learn the affinity relationship, reconstruction relationship, and projection matrix. In this model, we actively assign neighbors for each sample and learn the inter-reconstruction coefficients between each sample and their neighbors with the same label information in the process of dimensionality reduction. Specifically, a sparse constraint is employed to ensure the sparsity of neighbors and reconstruction coefficients. The whitening constraint is imposed on the projection matrix to remove the relevance between features. An iterative algorithm is proposed to solve this method. Extensive experiments on toy data and public datasets show the superiority of the proposed method.

Automated summary

This article proposes a joint model to simultaneously learn the affinity relationship, reconstruction relationship, and projection matrix. It addresses the limitations of reconstruction graphs in terms of local sparsity of coefficients and accuracy of manifold structure, and improves the dimensionality reduction process with sparse and whitening constraints.