Subspace segmentation based metric learning

Distance Metric Learning (DML) has been successfully applied in a variety of computer vision and image processing tasks. Laplacian Regularized Metric Learning (LRML) computes a distance metric by satisfying given sets of pairwise similarity and dissimilarity constraints while preserving the topological structure of the given data via a Laplacian regularizer which is dependent on an affinity matrix. This paper addresses the problem of semi-supervised DML using LRML for image data sampled from a union of low-dimensional subspaces by computing the affinity matrix using a self-representation based graph instead of traditional graph used in LRML, resulting in two variants of LRML called as L-NNLRS and L-NLSP.