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In this paper, we extend the popular dictionary pair learning (DPL) into thescenario of twin-projective latent flexible DPL under a structuredtwin-incoherence. Technically, a novel framework called Twin-Projective LatentFlexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherenceconstrained flexibly-relaxed reconstruction error to avoid the possibleover-fitting issue and produce accurate reconstruction. In this setting, ourTP-DPL integrates the twin-incoherence based latent flexible DPL and the jointembedding of codes as well as salient features by twin-projection into aunified model in an adaptive neighborhood-preserving manner. As a result,TP-DPL unifies the salient feature extraction, representation andclassification. The twin-incoherence constraint on codes and features canexplicitly ensure high intra-class compactness and inter-class separation overthem. TP-DPL also integrates the adaptive weighting to preserve the localneighborhood of the coefficients and salient features within each classexplicitly. For efficiency, TP-DPL uses Frobenius-norm and abandons the costlyl0/l1-norm for group sparse representation. Another byproduct is that TP-DPLcan directly apply the class-specific twin-projective reconstruction residualto compute the label of data. Extensive results on public databases show thatTP-DPL can deliver the state-of-the-art performance.

Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification