Detecting Changes in Unlabeled Data Streams Using Martingale
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1912-1916.
Abstract
The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass incremental change-detection algorithm (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the clustering algorithm as data points are streaming, and (iii) works well for high-dimensional data streams. To enhance the performance of the martingale change detection method, the multiple martingale test method using multiple views is proposed. Experimental results show (i) the feasibility of the martingale method for detecting changes in unlabeled data streams, and (ii) the multiple-martingale test method compares favorably with alternative methods using the recall and precision measures for the video-shot change detection problem.
@inproceedings{ijcai2007p308,
title = {Detecting Changes in Unlabeled Data Streams Using Martingale},
author = {Ho, Shen-Shyang and Wechsler, Harry},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1912--1916},
year = {2007},
url = {https://ijcai.org/proceedings/2007/308},
}
TY - CPAPER TI - Detecting Changes in Unlabeled Data Streams Using Martingale AU - Ho, Shen-Shyang AU - Wechsler, Harry T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1912 EP - 1916 UR - https://ijcai.org/proceedings/2007/308 ER -Copied
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