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Isao Higuchi

dblp:39/1950 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Representation and self-supervised learning · 50% Efficient and distributed learning · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
adaptive parameter selection		 0.012004
Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters · J. Mach. Learn. Res. 2004
Machine learning › Representation and self-supervised learning › component analysis
robust principal component analysis		 0.012004
Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters · J. Mach. Learn. Res. 2004

Methods — techniques the papers use, named apart from their topics

robust estimation · 0.0principal component analysis · 0.0
YearPublicationVenuePosition
2004 Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters
Isao Higuchi, Shinto Eguchi
 J. Mach. Learn. Res.1
1998 The Influence Function of Principal Component Analysis by Self-Organizing Rule
abstract
This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.
Isao Higuchi, Shinto Eguchi
 Neural Comput.1