Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding analysis (FDSNE) method for feature extraction is proposed in this paper by improving the existing DSNE method. The proposed algorithm adopts an alternative probability distribution model constructed based on its K-nearest neighbors from the interclass and intraclass samples. Furthermore, FDSNE is extended to nonlinear scenarios using the kernel trick and then kernel-based methods, that is, KFDSNE1 and KFDSNE2. FDSNE, KFDSNE1, and KFDSNE2 are evaluated in three aspects: visualization, recognition, and elapsed time. Experimental results on several datasets show that, compared with DSNE and MSNP, the proposed algorithm not only significantly enhances the computational efficiency but also obtains higher classification accuracy.

In this paper, we propose a new classifier named kernel group sparse representation via structural and non-convex constraints (KGSRSN) for image recognition. The new approach integrates both group sparsity and structure locality in the kernel feature space and then penalties a non-convex function to the representation coefficients. On the one hand, by mapping the training samples into the kernel space, the so-called norm normalization problem will be naturally alleviated. On the other hand, an interval for the parameter of penalty function is provided to promote more sparsity without sacrificing the uniqueness of the solution and robustness of convex optimization. Our method is computationally efficient due to the utilization of the Alternating Direction Method of Multipliers (ADMM) and Majorization-Minimization (MM). Experimental results on three real-world benchmark datasets, i.e., AR face database, PIE face database and MNIST handwritten digits database, demonstrate that KGSRSN can achieve more discriminative sparse coefficients, and it outperforms many state-of-the-art approaches for classification with respect to both recognition rates and running time.

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Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods.