4 citations · 4 across the 1 of their papers we have counts for
3 papers
stat.ML2019★ 4 cited
Solving Interpretable Kernel Dimension Reduction
Chieh Wu, Jared Miller, Yale Chang +2
Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing n…
stat.ML2019
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
Chieh Wu, Jared Miller, Yale Chang +2
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of diffe…
math.OC2019
Chordal Decomposition in Rank Minimized Semidefinite Programs with Applications to Subspace Clustering
Jared Miller, Yang Zheng, Biel Roig-Solvas +2
Semidefinite programs (SDPs) often arise in relaxations of some NP-hard problems, and if the solution of the SDP obeys certain rank constraints, the relaxation will be tight. Decom…