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Video based human action has many developments in recent years. Different data sets and algorithms have been evaluated for various approaches. A lot of research work has been published by different authors but still there is need of improvement and amendment. In this article we provide a new algorithm for human body movement which takes in a continuous stream of body poses and performs online action...
Minor component analysis (MCA) is an important feature extraction technique which has been widely applied in data analysis fields. MCA neural networks generally are used to extract online minor component in term of adapting the demands of real time and decreasing computational complexity. However, the MCA learning algorithm can produce complicated dynamical behavior under some conditions, such as...
Independent component analysis (ICA) neural network is an important method for separating the mixed signal into independent components. However, the ICA neural networks can produce complicated dynamical behavior under certain conditions, such as the periodic oscillation, bifurcation and chaos. This paper introduces the chaos control of a class of ICA learning algorithm, Hyvarinen and Oja's ICA namely,...
LMSER (least mean square error reconstruction) PCA (principal component analysis) algorithm is a learning algorithm which is generally used to extract principal components of data. However, the algorithm can produce complicated dynamical behavior under certain conditions, such as the periodic oscillation, bifurcation and chaos. This paper introduces the chaos control of LMSER PCA, and the stability...
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 learning algorithm for estimating adaptively...
Principal component analysis (PCA) by neural networks is one of the most frequently used feature extracting methods. To process huge data sets, many learning algorithms based on neural networks for PCA have been proposed. However, traditional algorithms are not globally convergent. In this paper, a new PCA learning algorithm based on cascade recursive least square (CRLS) neural network is proposed...
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