Abstract
This letter presents a new minor component analysis algorithm that is based on the transformation of the known minor subspace analysis algorithm. The minor subspace analysis method that is adopted is the one proposed by Douglas, Kung, and Amari. The proposed minor component analysis algorithm extracts N minor components of the K-dimensional vector stationary random process N<K. Since the chosen minor subspace analysis method is fully homogeneous from the point of view of the individual neuron, the proposed minor component analysis method is also fully homogeneous from the point of view of the individual neuron.
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