A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data

被引:72
作者
Iyengar, Satish G. [1 ]
Varshney, Pramod K. [1 ]
Damarla, Thyagaraju [2 ]
机构
[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY 13244 USA
[2] USA, Res Lab, Adelphi, MD 20783 USA
关键词
Copula theory; hypothesis testing; Kullback-Leibler divergence; multibiometrics; multimodal signals; multisensor fusion; statistical dependence; FUSION; MODEL;
D O I
10.1109/TSP.2011.2105483
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
We present a parametric framework for the joint processing of heterogeneous data, specifically for a binary classification problem. Processing such a data set is not straightforward as heterogeneous data may not be commensurate. In addition, the signals may also exhibit statistical dependence due to overlapping fields of view. We propose a copula-based solution to incorporate statistical dependence between disparate sources of information. The important problem of identifying the best copula for binary classification problems is also addressed. Computer simulation results are presented to demonstrate the feasibility of our approach. The method is also tested on real-data provided by the National Institute of Standards and Technology (NIST) for a multibiometric face recognition application. Finally, performance limits are derived to study the influence of statistical dependence on classification performance.
引用
收藏
页码:2308 / 2319
页数:12
相关论文
共 40 条
[1]
[Anonymous], 2001, Correlation and dependence
[2]
[Anonymous], 2005, P CVPR
[3]
AREA ABOVE ORDINAL DOMINANCE GRAPH AND AREA BELOW RECEIVER OPERATING CHARACTERISTIC GRAPH [J].
BAMBER, D .
JOURNAL OF MATHEMATICAL PSYCHOLOGY, 1975, 12 (04) :387-415
[4]
A graphical model for audiovisual object tracking [J].
Beal, MJ ;
Jojic, N ;
Attias, H .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2003, 25 (07) :828-836
[5]
Extraction of audio features specific to speech production for multimodal speaker detection [J].
Besson, Patricia ;
Popovici, Vlad ;
Vesin, Jean-Marc ;
Thiran, Jean-Philippe ;
Kunt, Murat .
IEEE TRANSACTIONS ON MULTIMEDIA, 2008, 10 (01) :63-73
[6]
Bouye E., 2000, SSRN Electron. J, P1
[7]
The use of the area under the roc curve in the evaluation of machine learning algorithms [J].
Bradley, AP .
PATTERN RECOGNITION, 1997, 30 (07) :1145-1159
[8]
Brunel N, 2005, INT CONF ACOUST SPEE, P717
[9]
From error probability to information theoretic (multi-modal) signal processing [J].
Butz, T ;
Thiran, JP .
SIGNAL PROCESSING, 2005, 85 (05) :875-902
[10]
LARGE-SAMPLE THEORY - PARAMETRIC CASE [J].
CHERNOFF, H .
ANNALS OF MATHEMATICAL STATISTICS, 1956, 27 (01) :1-22