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To investigate the relationship between worry tendency and sleep quality and the mediating effect of state-trait anxiety, 1072 adolescents and young adults from Jiangxi and Fujian Provinces in China were administered questionnaires pertaining to worry tendency, sleep quality, and state-trait anxiety. The results showed significant grade differences for worry tendency, sleep quality, and state-trait anxiety. Worry tendency was negatively associated with sleep quality, which was mediated by state anxiety and trait anxiety. There is a need for interventions that aim to reduce the level of worry tendency to ensure good sleep quality and the progression from worry tendency to anxiety and to poor sleep quality.

By combining linear discriminant analysis and Kmeans into a coherent framework, a dimension reduction algorithm was recently proposed to select the most discriminative subspace. This algorithm utilized the clustering method to generate cluster labels and after that employed discriminant analysis to do subspace selection. However, we found that this algorithm only considers the information of global structure, and does not take into account the information of local structure. In order to overcome the shortcoming mentioned above, this paper presents a dimension reduction algorithm preserving both global and local clustering structure. Our algorithm is an unsupervised linear dimension reduction algorithm suitable for the data with cloud distribution. In the proposed algorithm, the Kmeans clustering method is adopted to generate the clustering labels for all data in the original space. And then, the obtained clustering labels are utilized to describe the global and local clustering structure. Finally, the objective function is established to preserve both the local and global clustering structure. By solving this objective function, the projection matrix and the corresponding subspace are yielded. In this way, the global and local information of the clustering structure are integrated into the process of the subspace selection, in fact, the structure discovery and the subspace selection are performed simultaneously in our algorithm. Encouraging experimental results are achieved on the artificial dataset, real-life benchmark dataset and AR face dataset.

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Traditional pattern recognition involves two tasks: clustering learning and classification learning.Clustering result can enhance the generalization ability of classification learning, while the class information can improve the accuracy of clustering learning. Hence, both learning methods can complement each other. To fuse the advantages of both learning methods together, many existing algorithms have been developed in a sequential fusing way by first optimizing the clustering criterion and then the classification criterion associated with the obtained clustering results. However, such kind of algorithms naturally fails to achieve the simultaneous optimality for two criteria, and thus have to sacrifice either the clustering performance or the classification performance. To overcome that problem, in this paper, we present a multi-objective simultaneous learning framework (named MSCC) for both clustering and classification learning. MSCC utilizes multiple objective functions to formulate the clustering and classification problems, respectively, and more importantly it employs the Bayesian theory to make these functions all only dependent on a set of the same parameters, i.e., clustering centers which play a role of the bridge connecting the clustering and classification learning. By simultaneously optimizing the clustering centers embedded in these functions, not only the effective clustering performance but also the promising classification performance can be simultaneously attained. Furthermore, from the multiple Pareto-optimality solutions obtained in MSCC, we can get an interesting observation that there is complementarity to great extent between clustering and classification learning processes. Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC.