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In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect …