Abstract
In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms searchbased algorithms on real-world time series data.
Original language | English |
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Title of host publication | IJCAI 2016 |
Subtitle of host publication | Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence |
Editors | Gerhard Brewka |
Place of Publication | Palo Alto, CA |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 2322-2328 |
Number of pages | 7 |
ISBN (Electronic) | 9781577357704, 9781577357711 |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Joint Conferences on Artificial Intelligence (25th : 2016) - New York, United States Duration: 9 Jul 2016 → 15 Jul 2016 |
Conference
Conference | International Joint Conferences on Artificial Intelligence (25th : 2016) |
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Country/Territory | United States |
City | New York |
Period | 9/07/16 → 15/07/16 |