output
20162020
most citedInvariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

314 citations

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5 papers · 1 filter

stat.ML2019

Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs

Wenkai Xu, Gang Niu, Aapo Hyvärinen +1

Summarizing large-scaled directed graphs into small-scale representations is a useful but less studied problem setting. Conventional clustering approaches, which based on "Min-Cut"…

stat.ML2019

Active learning for enumerating local minima based on Gaussian process derivatives

Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura +1

We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging…

stat.ML20195 cited

Robust Graph Embedding with Noisy Link Weights

Akifumi Okuno, Hidetoshi Shimodaira

We propose -graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment -score reduces the influence o…

stat.ML20172 cited

Estimation of interventional effects of features on prediction

Patrick Blöbaum, Shohei Shimizu

The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models…

stat.ML20169 cited

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…