454 citations · 5.5k across the 247 of their papers we have counts for
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Feature Selection via L1-Penalized Squared-Loss Mutual Information
Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to s…
Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information
Makoto Yamada, Leonid Sigal, Michalis Raptis +1
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatic…
Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness
Taiji Suzuki, Masashi Sugiyama
We investigate the learning rate of multiple kernel learning (MKL) with and elastic-net regularizations. The elastic-net regularization is a composition of an -reg…
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Song Liu, Makoto Yamada, Nigel Collier +1
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection a…
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal +2
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operato…