5 accepted papers
Multi-modal clustering, which integrates information from diverse sources and feature modalities, has shown great potential in data mining and computer vision. However, existing methods relying on single-granularity relationships often struggle with complex data distributions, leading to limited per…
Despite the superior capability in complementary information exploration and consistent clustering structure learning, most current weight-based multi-modal clustering methods still contain three limitations: 1) lack of trustworthiness in learned weights; 2) isolated view weight learning; 3) extra w…
In an era of increasingly diverse information sources, multi-modal clustering (MMC) has become a key technology for processing multi-modal data. It can apply and integrate the feature information and potential relationships of different modalities. Although there is a wealth of research on MMC, due…
Multi-view clustering (MVC) is a long-standing topic in machine learning and data mining community, focusing on investigating and utilizing the relationships among views for final consistent data cluster structure discovery. Generally, weighted MVC is one of the popular methods working by learning a…
Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the powerful algorithms can be realized by a combinati…