Learning Structured TwinIncoherent TwinProjective Latent Dictionary Pairs for Classification In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twinprojective latent flexible DPL under a structured twinincoherence. Technically, a novel framework called TwinProjective Latent Flexible DPL (TPDPL) is proposed, which minimizes the twinincoherence constrained flexiblyrelaxed reconstruction error to avoid the possible overfitting issue and produce accurate reconstruction. In this setting, TPDPL integrates the twinincoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twinprojection into a unified model in an adaptive neighborhoodpreserving manner. Therefore, TPDPL can unify the procedures of salient feature representation and classification. The twinincoherence constraint on coefficients and features can explicitly ensure high intraclass compactness and interclass separation over them. TPDPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TPDPL selects the Frobeniusnorm and abandons the costly l0l1norm for group sparse representation. Another byproduct is that TPDPL can directly apply the classspecific twinprojective reconstruction residual to compute the label of data. Extensive results on public databases show that TPDPL can deliver the stateoftheart performance.

Adaptive sparse and dense hybrid representation with nonconvex optimization Sparse representation has been widely used in signal processing, pattern recognition and computer vision etc. Excellent achievements have been made in both theoretical researches and practical applications. However, there are two limitations on the application of classification. One is that sufficient training samples are required for each class, and the other is that samples should be uncorrupted. In order to alleviate above problems, a sparse and dense hybrid representation (SDR) framework has been proposed, where the training dictionary is decomposed into a classspecific dictionary and a nonclassspecific dictionary. SDR puts l1 constraint on the coefficients of classspecific dictionary. Nevertheless, it overemphasizes the sparsity and overlooks the correlation information in classspecific dictionary, which may lead to poor classification results. To overcome this disadvantage, an adaptive sparse and dense hybrid representation with nonconvex optimization (ASDRNO) is proposed in this paper. The trace norm is adopted in classspecific dictionary, which is different from general approaches. By doing so, the dictionary structure becomes adaptive and the representation ability of the dictionary will be improved. Meanwhile, a nonconvex surrogate is used to approximate the rank function in dictionary decomposition in order to avoid a suboptimal solution of the original rank minimization, which can be solved by iteratively reweighted nuclear norm (IRNN) algorithm. Extensive experiments conducted on benchmark data sets have verified the effectiveness and advancement of the proposed algorithm compared with the stateoftheart sparse representation methods.