9 citations · 26 across the 11 of their papers we have counts for
3 papers · 1 filter
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
Consistent Collective Matrix Completion under Joint Low Rank Structure
Suriya Gunasekar, Makoto Yamada, Dawei Yin +1
We address the collective matrix completion problem of jointly recovering a collection of matrices with shared structure from partial (and potentially noisy) observations. To ensur…
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Makoto Yamada, Avishek Saha, Hua Ouyang +2
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of t…