Subspace learning via low rank projections for dimensionality reduction

Subspace learning algorithms aim at finding low dimensional linear manifolds that are representative of the data at hand. In this paper we propose a semi-supervised approach that fits any given dataset to a low dimensional subspace while maintaining class separability. Our approach has no tunable parameters as against many existing subspace learning algorithms which obviates the need for cross-validation. We apply our algorithm to the problem of face recognition. We perform both qualitative as well as quantitative experiments on multiple real world datasets. For qualitative analysis we visualize the class separability of binary and multi-class projected data. For quantitative analysis, we perform classification experiments on projected data and achieve state-of-the-art results compared to popular existing dimensionality reduction methods.