Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder

Deep clustering attempts to capture the feature representation that benefits the clustering issue. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still present some shortcomings, such as the lack of consideration of local structure retention and sparse characteristics of input data. To this end, we propose a deep stacked sparse embedded clustering method in this paper, which considers both the local structure preservation and sparse property of inputs. The proposed method is trained to capture the feature representation for an input data by the guidance of the clustering and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and preserve the local structure. Besides, sparse constraint is added to the encoder to avoid learning of unimportant features. Through simultaneously minimizing the reconstruction and clustering loss, the proposed method is able to jointly learn the clustering oriented features and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consists of seven clustering methods and six publicly available image data sets. Eventually, comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in the proposed method.

Automated summary

The paper proposes a deep stacked sparse embedded clustering method that considers both local structure preservation and input sparsity. The deep learning approach jointly learns clustering-oriented features and optimizes cluster label assignments by minimizing both the reconstruction and clustering loss. Comprehensive experiments validate the effectiveness of introducing sparsity and preserving local structure in the proposed method.