Improved Deep Embedded K-Means Clustering with Implicit Orthogonal Space Transformation

The deep clustering algorithm can learn the latent embedded features of the data through the autoencoder, and cluster the data according to the similarity of the latent features. However, the feature information obtained by the autoencoder may not have a better value for the clustering algorithm and is not suitable for clustering, which greatly reduces the clustering effect. This paper proposes a deep K-means clustering algorithm with implicitly embedded space transformation to answer this question. We implicitly transform the latent feature space into a new type of space that is more friendly to the clustering task, which preserves space invariance. This implicit transformation is done through an orthogonal transformation matrix. The orthogonal transformation matrix is composed of the eigenvectors of the intra-class scattering matrix and the inter-class scattering matrix. In the new space, clusters can be better separated by cluster cohesion and inter-cluster difference. We alternately optimize feature acquisition and clustering to adjust the embedding space and disperse the embedding points, to enrich the clustering information in the latent feature space. Experimental results show that our proposed algorithm can produce better high-quality clusters than many current correlation clustering algorithms on the same experimental dataset.