A Robust PCA by LMSER Learning with Iterative Error Reinforcement y
نویسندگان
چکیده
We propose an approach for performing adaptive principal component extraction. By this approach, the Least Mean Squared Error Reconstruction (LMSER) Principle is implemented in a successive way such that the reconstruction error is fedback as inputs for training the network's weights. Simulations results have shown that this type of LMSER implementation can perform Robust Principal Component Analysis (PCA) which is capable of resisting strong outliers.
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