Deep Friendly Embedding Space for Clustering

Deep clustering has powerful capabilities of dimensionality reduction and non-linear feature extraction, superior to conventional shallow clustering algorithms. Deep learning and clustering can be unified through one objective function, significantly improving clustering performance. However, the features of embedding space may have redundancy and ignore preserved manifold. Besides, the features lack discriminative, which hinders the clustering performance. To solve the above problems, the paper proposes a novel algorithm that improves the discrimination of features, filters redundant features and protects manifold structures for clustering. Firstly, it reduces the dimensionality in the embedding again to filter redundant and preserve the manifold for the features. Then it improves the discriminative of the representation by reducing the intra-class distance. Performance evaluation is carried out on four benchmark datasets and a case study of engineering applications. Comparing with state-of-the-art algorithms indicates that our algorithm performs favorably and demonstrates good potential for real-world applications.