3 accepted papers
The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and compared with other existing relaxations, it can flexibly incorporate class information. We prove that this relaxation forms a manifold,…
Anchor-based clustering methods have gained attention for their efficiency in subspace, multi-view, and ensemble clustering tasks. Most existing methods focus on using anchors to reduce computational complexity in the original data space. However, clustering directly on anchors, followed by label pr…
Anchor graph clustering has gained significant attention due to its effectiveness and efficiency. As the most representative points in original data, anchors are applied to connect the sample space to label space. However, when noise is present in original data, the anchor-refined label spreading me…