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A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability
December 2010 (vol. 32 no. 12)
pp. 2113-2127
ASCII Text
x 
 
Shen-Shyang Ho, Harry Wechsler, "A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2113-2127, December, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2010.48,
author = {Shen-Shyang Ho and Harry Wechsler},
title = {A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {12},
issn = {0162-8828},
year = {2010},
pages = {2113-2127},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.48},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability
IS - 12
SN - 0162-8828
SP2113
EP2127
EPD - 2113-2127
A1 - Shen-Shyang Ho,
A1 - Harry Wechsler,
PY - 2010
KW - Change detection
KW - data stream
KW - exchangeability
KW - hypothesis testing
KW - martingale
KW - classification
KW - regression
KW - clustering
KW - support vector machine.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Shen-Shyang Ho, Univesity of Maryland Institute for Advanced Computer Studies, College Park
Harry Wechsler, George Mason University, Fairfax
In a data streaming setting, data points are observed sequentially. The data generating model may change as the data are streaming. In this paper, we propose detecting this change in data streams by testing the exchangeability property of the observed data. Our martingale approach is an efficient, nonparametric, one-pass algorithm that is effective on the classification, cluster, and regression data generating models. Experimental results show the feasibility and effectiveness of the martingale methodology in detecting changes in the data generating model for time-varying data streams. Moreover, we also show that: 1) An adaptive support vector machine (SVM) utilizing the martingale methodology compares favorably against an adaptive SVM utilizing a sliding window, and 2) a multiple martingale video-shot change detector compares favorably against standard shot-change detection algorithms.

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Index Terms:
Change detection, data stream, exchangeability, hypothesis testing, martingale, classification, regression, clustering, support vector machine.
Citation:
Shen-Shyang Ho, Harry Wechsler, "A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2113-2127, Dec. 2010, doi:10.1109/TPAMI.2010.48
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