Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
This work addresses classification tasks in machine learning by extending dictionary pair learning, but it appears incremental as it builds upon existing DPL methods with modifications like twin-incoherence and flexible relaxation.
The paper tackles the problem of improving dictionary pair learning for classification by proposing a Twin-Projective Latent Flexible DPL (TP-DPL) framework that minimizes twin-incoherence constrained reconstruction error to avoid over-fitting and enhance accuracy, achieving state-of-the-art performance on public databases.
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. As a result, TP-DPL unifies the salient feature extraction, representation and classification. The twin-incoherence constraint on codes and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve the local neighborhood of the coefficients and salient features within each class explicitly. For efficiency, TP-DPL uses Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.