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Minor component analysis (MCA) is a powerful statistical tool for signal processing and data analysis. Convergence of MCA learning algorithms is an important issue in practical applications. In this paper, we will propose a simple MCA learning algorithm to extract minor component from input signals. Dynamics of the proposed MCA learning algorithm are(More)
In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of(More)
Extracting multiple minor components from the input signal is quite useful for many practical applications. In this paper, a globally convergent MCA algorithm that can extract multiple minor components sequentially is proposed. Convergence of this MCA algorithm is analyzed via the deterministic discrete time (DDT) method. Sufficient conditions are obtained(More)