Clustering with Similarity Preserving
This work addresses a specific bottleneck in graph-based clustering for data analysis applications, representing an incremental improvement.
The paper tackles the problem of sub-optimal performance in graph-based clustering due to non-similarity-preserving kernel-based graph learning by proposing a discriminative method that preserves pairwise similarities adaptively and unifies clustering with graph learning, achieving improved accuracy on multiple datasets.
Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several datasets.