Consensus graph learning for multi-view clustering
Multi-view clustering, which exploits the multi-view information to partition data into their
clusters, has attracted intense attention. However, most existing methods directly learn a …
clusters, has attracted intense attention. However, most existing methods directly learn a …
High-order correlation preserved incomplete multi-view subspace clustering
Incomplete multi-view clustering aims to exploit the information of multiple incomplete views
to partition data into their clusters. Existing methods only utilize the pair-wise sample …
to partition data into their clusters. Existing methods only utilize the pair-wise sample …
Reconsidering representation alignment for multi-view clustering
Aligning distributions of view representations is a core component of today's state of the art
models for deep multi-view clustering. However, we identify several drawbacks with naively …
models for deep multi-view clustering. However, we identify several drawbacks with naively …
Towards adaptive consensus graph: multi-view clustering via graph collaboration
Multi-view clustering is a long-standing important task, however, it remains challenging to
exploit valuable information from the complex multi-view data located in diverse high …
exploit valuable information from the complex multi-view data located in diverse high …
Tensorial multi-view clustering via low-rank constrained high-order graph learning
G Jiang, J Peng, H Wang, Z Mi… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-view clustering aims to partition multi-view data into different categories by optimally
exploring the consistency and complementary information from multiple sources. However …
exploring the consistency and complementary information from multiple sources. However …
Tensor completion via complementary global, local, and nonlocal priors
Completing missing entries in multidimensional visual data is a typical ill-posed problem that
requires appropriate exploitation of prior information of the underlying data. Commonly used …
requires appropriate exploitation of prior information of the underlying data. Commonly used …
End-to-end adversarial-attention network for multi-modal clustering
Multi-modal clustering aims to cluster data into different groups by exploring complementary
information from multiple modalities or views. Little work learns the deep fused …
information from multiple modalities or views. Little work learns the deep fused …
Tensorized multi-view subspace representation learning
Self-representation based subspace learning has shown its effectiveness in many
applications. In this paper, we promote the traditional subspace representation learning by …
applications. In this paper, we promote the traditional subspace representation learning by …
Multiview subspace clustering by an enhanced tensor nuclear norm
Despite the promising preliminary results, tensor-singular value decomposition (t-SVD)-
based multiview subspace is incapable of dealing with real problems, such as noise and …
based multiview subspace is incapable of dealing with real problems, such as noise and …
Tensorized incomplete multi-view clustering with intrinsic graph completion
Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a
consensus representation from different views but ignore the important information hidden in …
consensus representation from different views but ignore the important information hidden in …