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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)
This work proposes a novel dual-channel time-spread echo method for audio watermarking, aiming to improve robustness and perceptual quality. At the embedding stage, the host audio signal is divided into two subsignals, which are considered to be signals obtained from two virtual audio channels. The watermarks are implanted into the two subsignals(More)
Principal component analysis (PCA) and minor component analysis (MCA) are two important statistical tools which have many applications in the fields of signal processing and data analysis. PCA and MCA neural networks (NNs) can be used to online extract principal component and minor component from input data. It is interesting to develop generalized learning(More)
The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks(More)
The convergence of minor-component analysis (MCA) algorithms is an important issue with bearing on the use of these methods in practical applications. This correspondence studies the convergence of Feng's MCA learning algorithm via a corresponding deterministic discrete-time (DDT) system. Some sufficient convergence conditions are obtained for Feng's MCA(More)