Local linear embedding algorithm of mutual neighborhood based on multi-information fusion metric

Abstract

Local linear embedding (LLE) algorithm is an effective tool, which mines low-dimensional features in high-dimensional space. However, the local region and inner structure directly affect the performance of the LLE algorithm. To address this problem, the LLE algorithm of mutual neighborhood by employing multi-information fusion metric (MIFM-MNLLE) is proposed. First, the Euclidean distance and cosine similarity method are combined to evaluate the similary among samples, by which the accuracy of selected neighbors can be improved. Subsequently, the idea of mutual neighbor structure is utilized to construct the neighbor and the mutual neighbor graph of the sample to describe the internal structure of the data set. Finally, the coefficient of LLE is rectified according to the mutual neighborhood relationship between the sample and its neighbors, so as to extract significant features effectively. Extensive experimental results show that the proposed method has better performance compared with the existing methods on bearing datasets.