The block generative topographic mapping

被引：0
|
作者：
Priam, Rodolphe [1 ]
Nadif, Mohamed [2 ]
Govaert, Gerard [3 ]
机构：
[1] Univ Poitiers, LMA Poitiers UMR 6086, F-86962 Futuroscope, France
[2] Univ Paris 05, UFR Mathemat Informat, F-75006 Paris, France
[3] Heudiasyc, UTC, UMR 6599, F-60205 Compiegne, France
来源：
ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION, PROCEEDINGS | 2008年 / 5064卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a generative model and its estimation allowing to visualize binary data. Our approach is based on the Bernoulli block mixture model and the probabilistic self-organizing maps. This leads to an efficient variant of Generative Topographic Mapping. The obtained method is parsimonious and relevant on real data.
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收藏
页码：13 / +
页数：3
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