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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...
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