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The minor component analysis (MCA) deals with the recovery of the eigenvector associated to the smallest eigenvalue of the autocorrelation matrix of the input dada, and it is a very important tool for signal processing and data analysis. This brief analyzes the convergence and stability of a class of self-stabilizing MCA algorithms via a deterministic discrete-time (DDT) method. Some sufficient conditions...
A new algorithm, Laplacian minmax discriminant projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a linear transformation which is an extension of linear discriminant analysis (LDA). Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After...
Wireless sensor network is becoming increasingly important in applications such as environmental monitoring and traffic control. They collect a large amount of sensor data, e.g., temperature and pressure. Nearby sensor nodes monitoring an environmental feature typically measure correlated values. In this paper, we propose a new location-aware-based data clustering (LABDC) algorithm to analyze the...
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