Large-scale graph database indexing based on T-mixture model and ICA

被引：0

作者：

Luo, Bin ^{[1
]}

Zheng, Aihua ^{[1
]}

Tang, Jin ^{[1
]}

Zhao, Haifeng ^{[1
]}

机构：

[1] Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

来源：

PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS | 2007年

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1109/ICIG.2007.179

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 proposes an indexing scheme based on t-mixture model and ICA, which is more robust than Gaussian mixture modeling when atypical points (or outliers) exist or the set of data has heavy tail. This indexing scheme combines optimized vector quantizer and probabilistic approximate-based indexing, scheme. Experimental results on large-scale graph database show a notable efficiency improvement with optimistic precision.

引用

页码：815 / +

页数：3

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