Semi-supervised information-maximization clustering
type: Item
instance of: scholarly article
title: Semi-supervised information-maximization clustering
zbMATH Open document ID: 1325.68207
author: Daniele Calandriello
author: Gang Niu
author: Masashi Sugiyama
published in: Neural Networks
publication date: 2015-11-06T00:00:00Z
full work available at URL: https://arxiv.org/abs/1304.8020
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6505389
zbMATH Keywords: clustering
zbMATH Keywords: information maximization
zbMATH Keywords: squared-loss mutual information
zbMATH Keywords: semi-supervised
Wikidata QID: Q51072383
MaRDI profile type: Publication
OpenAlex ID: W2170667765
arXiv ID: 1304.8020
cites work: Q5601960
cites work: Robust and efficient estimation by minimising a density power divergence
cites work: Q5539510
cites work: On Information and Sufficiency
cites work: Q3093203
cites work: Q5617409
cites work: A Mathematical Theory of Communication
cites work: Machine learning with squared-loss mutual information
cites work: Information-Maximization Clustering Based on Squared-Loss Mutual Information
cites work: Density ratio estimation in machine learning. Foreword by Thomas G. Dietterich
cites work: Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation
DBLP publication ID: journals/nn/CalandrielloNS14
DOI: 10.1016/J.NEUNET.2014.05.016
description: scientific article