39 citations · 157 across the 35 of their papers we have counts for
Showing 2019 · stat.MLShow all
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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★ 11 cited
Iterative Spectral Method for Alternative Clustering
Chieh Wu, Stratis Ioannidis, Mario Sznaier +3
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Altern…
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…