{"type":"ScholarlyArticle","title":"Unsupervised Feature Learning from Time Series","authors":[{"name":"Qin Zhang","slug":"qin-zhang","given":"Qin","family":"Zhang","affiliation":""},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":""},{"name":"Hong Yang","slug":"hong-yang","given":"Hong","family":"Yang","affiliation":""},{"name":"Yingjie Tian","slug":"yingjie-tian","given":"Yingjie","family":"Tian","affiliation":""},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":""}],"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 timeseries 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 search-based algorithms on real-world time series data.","doi":null,"url":"https://ijcai.org/proceedings/2016/331","pdf":"https://ijcai.org/proceedings/2016/0331.pdf","markdown":"https://ijcai.org/proceedings/2016/331.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/331","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":331,"pages":{"first":2322,"last":2327},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}