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Kernel independent component analysis (KICA) detects primary independent components of data by minimizing kernelized canonical correlation of random variables in a reproducing kernel Hilbert space. KICA has been widely used in many practical tasks, e.g., blind source separation and speech recognition. However, the dense kernel matrix in traditional KICA causes high computational complexity which prohibits...
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