Dec 30, 2023 · This paper proposes an effective consensus learning model, referred to as tensor-based consensus learning for incomplete multi-view clustering (TCLIMC).
Then a relaxed spectral clustering model is introduced to obtain a probability consensus representation with all positive elements that reflect the data ...
Wen, J., Zhang, Z., Xu, Y., Zhang, B., Fei, L., & Liu, H. (2019). Unified embedding alignment with missing views inferring for incomplete multi-view clustering.
The existing methods of incomplete multi- view clustering can be divided into three categories: ma- trix factorization-based, graph-based, and kernel-based. Ma-.
Dec 21, 2022 · In this article, we propose a low-rank tensor learning (LRTL) method that learns a consensus low-dimensional embedding matrix for IMVC. We first ...
Mar 27, 2024 · We propose a novel tensor-based multi-view graph learning framework that simultaneously considers consistency and specificity, while effectively eliminating ...
Missing: incomplete | Show results with:incomplete
In this article, we propose a low-rank tensor learning (LRTL) method that learns a consensus low-dimensional embedding matrix for IMVC. We first take advantage ...
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization ...
A Tensor-based Adaptive Consensus Graph Learning (TACGL) model, which combines representation matrices of multiple views into a representation tensor to ...
We propose a new approach, called Tensor-based Incomplete Multi-view Clustering with Low-rank data Reconstruction and Consistency guidance (TIMC-RC), ...