Oct 15, 2018 · In this paper, our proposed model learns an optimal weight for each view automatically without introducing an additive parameter as previous methods do.

In this paper, our proposed model learns an optimal weight for each view automatically without introducing an additive parameter as previous methods do.

This paper proposes a novel clustering method for handing corrupted multi-view data, hereafter referred to as Latent Low-Rank Proxy Graph Learning (LLPGL), ...

Furthermore, to deal with different level noises and outliers, we propose to use 'soft' capped norm, which caps the residual of outliers as a constant value and ...

Furthermore, to deal with different level noises and outliers, we propose to use 'soft' capped norm, which caps the residual of outliers as a constant value and ...

Self-weighted multi-view clustering with soft capped norm. https://doi.org/10.1016/j.knosys.2018.05.017 ·. Journal: Knowledge-Based Systems, 2018, p. 1-8.

This reporsity is a collection of state-of-the-art (SOTA), novel incomplete and complete multi-view clustering (papers, codes and datasets).

Xu, Tao, and Xu (2015) proposed a self- paced smoothed weighting scheme that dynamically assigns weights to views in clustering process for gradually training.

Nov 28, 2019 · By exploiting the capped-norm loss as the objec- tive, CAMVC could decrease the influence caused by noises and outliers. Huang et al. [28] ...

Multi-view clustering (MVC), which can exploit complementary information of different views to enhance the clustering performance, has attracted people's ...