The minor component is the eigenvector associated with the smallest eigenvalue of the covariance matrix of the input data. The minor component analysis (MCA) is a statistical method for extracting the minor component. Many neural networks have been proposed to solve MCA. However, there exists the problem of the divergence of the norm of the weight vector in these neural networks. In this paper, a modification to the well known MCA EXIN algorithm is presented by adjusting the learning rate. The modified MCA EXIN algorithm can guarantee that the norm of the weight vector of the neural network converges to a constant. Mathematical proofs and simulation results are given to show the convergence of the algorithm.
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