Real Deep K-means with Multiple Auto-Encoders

K-means clustering separates a set of samples into several groups based on the similarities between samples. To further assess the nonlinear correlation between high-dimensional samples, existing deep K-means algorithms just exploit an auto-encoder to extract the inherent features of samples, and then perform K-means on it. From this letter, we present the real deep K-means clustering model with K auto-encoders where K is the number of clusters, which is named as DKMA. Specifically, the centroid of each cluster is acted by one auto-encoder, rather than the constant vector in the traditional K-means. Each sample decides its category by choosing one auto-encoder which reconstructs the sample point best. The extensive experimental results indicate that the our present approach performs better than the other clustering algorithms.