Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

<abstract><p>The developing of DNA microarray technology has made it possible to study the cancer in view of the genes. Since the correlation between the genes is unconsidered, current unsupervised feature selection models may select lots of the redundant genes during the feature selecting due to the over focusing on genes with similar attribute. which may deteriorate the clustering performance of the model. To tackle this problem, we propose an adaptive feature selection model here in which reconstructed coefficient matrix with additional constraint is introduced to transform original data of high dimensional space into a low-dimensional space meanwhile to prevent over focusing on genes with similar attribute. Moreover, Alternative Optimization (AO) is also proposed to handle the nonconvex optimization induced by solving the proposed model. The experimental results on four different cancer datasets show that the proposed model is superior to existing models in the aspects such as clustering accuracy and sparsity of selected genes.</p></abstract>

Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multi-target tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.

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