no code implementations • 26 Apr 2022 • Shiye Wang, Changsheng Li, Yanming Li, Ye Yuan, Guoren Wang
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
no code implementations • 28 Oct 2021 • Yanming Li, Changsheng Li, Shiye Wang, Ye Yuan, Guoren Wang
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models.
no code implementations • 17 May 2018 • Kevin He, Jian Kang, Hyokyoung Grace Hong, Ji Zhu, Yanming Li, Huazhen Lin, Han Xu, Yi Li
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size.
no code implementations • 4 Nov 2016 • Yanming Li, Hyokyoung Hong, Jian Kang, Kevin He, Ji Zhu, Yi Li
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.