{"type":"ScholarlyArticle","title":"Improving Activity Discovery with Automatic Neighborhood Estimation","authors":[{"name":"David Minnen","slug":"david-minnen","given":"David","family":"Minnen","affiliation":"Georgia Institute of Technology GVU / College of Computing"},{"name":"Thad Starner","slug":"thad-starner","given":"Thad","family":"Starner","affiliation":"Georgia Institute of Technology GVU / College of Computing"},{"name":"Irfan A. Essa","slug":"irfan-a-essa","given":"Irfan A.","family":"Essa","affiliation":"Georgia Institute of Technology GVU / College of Computing"},{"name":"Charles Lee Isbell Jr.","slug":"charles-lee-isbell-jr","given":"Charles Lee Isbell","family":"Jr.","affiliation":"Georgia Institute of Technology GVU / College of Computing"}],"abstract":"A fundamental problem for artificial intelligence is identifying perceptual primitives from raw sensory signals that are useful for higher-level reasoning. We equate these primitives with initially unknown recurring patterns called motifs. Autonomously learning the motifs is difficult because their number, location, length, and shape are all unknown. Furthermore, nonlinear temporal warping may be required to ensure the similarity of motif occurrences. In this paper, we extend a leading motif discovery algorithm by allowing it to operate on multidimensional sensor data, incorporating automatic parameter estimation, and providing for motif-specific similarity adaptation. We evaluate our algorithm on several data sets and show how our approach leads to faster real world discovery and more accurate motifs compared to other leading methods.","doi":null,"url":"https://ijcai.org/proceedings/2007/452","pdf":"https://ijcai.org/proceedings/2007/0452.pdf","markdown":"https://ijcai.org/proceedings/2007/452.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/452","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":452,"pages":{"first":2814,"last":2818},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Bailey and Elkan, 1994] T.L. Bailey and C. Elkan. Fitting a mix- ture model by expectation maximization to discover motifs in biopolymers. In Second International Conference on Intelligent Systems for Molecular Biology, pages 28-36. AAAI Press, 1994.","title":"Fitting a mixture model by expectation maximization to discover motifs in biopolymers","authors":"T L Bailey, C Elkan","year":"1994","venue":"Second International Conference on Intelligent Systems for Molecular Biology","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bao and Intille, 2004] Ling Bao and Stephen S. Intille. Activity recognition from user-annotated acceleration data. In A. Ferscha and F. Mattern, editors, Pervasive, volume 3001 of Lecture Notes in Computer Science, pages 1-17. Springer, 2004.","title":"Activity recognition from user-annotated acceleration data","authors":"Ling Bao, Stephen S Intille","year":"2004","venue":"Pervasive","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blekas et al., 2003] K. Blekas, D. Fotiadis, and A. Likas. Greedy mixture learning for multiple motif discovery in biological se- quences. Biinformatics, 19(5), 2003.","title":"Greedy mixture learning for multiple motif discovery in biological sequences","authors":"K Blekas, D Fotiadis, A Likas","year":"2003","venue":"Biinformatics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chiu et al., 2003] B. Chiu, E. Keogh, and S. Lonardi. Probabilistic disovery of time series motifs. In Conf. on Knowledge Discovery in Data, pages 493-498, 2003.","title":"Probabilistic disovery of time series motifs","authors":"B Chiu, E Keogh, S Lonardi","year":"2003","venue":"Conf. on Knowledge Discovery in Data","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cohen et al., 2002] Paul R. Cohen, J. Tim Oates, and Carole R. Beal. Robots that learn meanings. In submitted to First Joint Con- ference on Autonomous Agents and Multiagent Systems, 2002.","title":"Robots that learn meanings","authors":"R Paul, J Tim Cohen, Carole R Oates, Beal","year":"2002","venue":"submitted to First Joint Conference on Autonomous Agents and Multiagent Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cormen et al., 1997] T. Cormen, C. Leiserson, and R. Rivest. In- troduction to Algorithms. The MIT Press, 1997.","title":"Introduction to Algorithms","authors":"T Cormen, C Leiserson, R Rivest","year":"1997","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Jensen et al., 2006] K.L. Jensen, M. P. Styczynski, Isidore Rigout- sos, and G.N. Stephanopoulos. A generic motif discovery algo- rithm for sequential data. Bioinformatics, 22(1):21-28, 2006. Figure 8: The motif discovered in the 8D fetal ECG data (the first four dimensions are shown) using automatic radius estimation.","title":"A generic motif discovery algorithm for sequential data","authors":"K L Jensen, M P Styczynski, Isidore Rigoutsos, G N Stephanopoulos","year":"2006","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Keogh and Folias, 2002] E. Keogh and T. Folias. UCR time series data mining archive, 2002.","title":"UCR time series data mining archive","authors":"E Keogh, T Folias","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Keogh et al., 2000] E. Keogh, K. Chakrabarti, M.J. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Infor- mation Systems, 3(3):263-286, 2000.","title":"Dimensionality reduction for fast similarity search in large time series databases","authors":"E Keogh, K Chakrabarti, M J Pazzani, S Mehrotra","year":"2000","venue":"Journal of Knowledge and Information Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Keogh et al., 2004] E. Keogh, S. Lonardi, and C. Ratanamahatana. Towards parameter-free data mining. In ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2004.","title":"Towards parameter-free data mining","authors":"E Keogh, S Lonardi, C Ratanamahatana","year":"2004","venue":"ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Keogh, 2006] E. Keogh. Personal communication, 2006.","title":null,"authors":"E Keogh","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lin et al., 2002] J. Lin, E. Keogh, S. Lonardi, and P. Patel. Finding motifs in time series. In Proceedings of the Second Workshop on Temporal Data Mining, Edmonton, Alberta, Canada, July 2002.","title":"Finding motifs in time series","authors":"J Lin, E Keogh, S Lonardi, P Patel","year":"2002","venue":"Proceedings of the Second Workshop on Temporal Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lin et al., 2003] J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A sym- bolic representation of time series, with implications for stream- ing algorithms. In 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, June 2003.","title":"A symbolic representation of time series, with implications for streaming algorithms","authors":"J Lin, E Keogh, S Lonardi, B Chiu","year":"2003","venue":"8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Minnen et al., 2003] D. Minnen, I. Essa, and T. Starner. Expec- tation grammars: Leveraging high-level expectations for activity recognition. In CVPR 2003, June 2003.","title":"Expectation grammars: Leveraging high-level expectations for activity recognition","authors":"D Minnen, I Essa, T Starner","year":"2003","venue":"CVPR 2003","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Minnen et al., 2006a] D. Minnen, T. Starner, I. Essa, and C. Isbell. Discovering characteristic actions from on-body sensor data. In International Symposium on Wearable Computers, pages 11-18, October 2006.","title":"Discovering characteristic actions from on-body sensor data","authors":"Minnen","year":"2006","venue":"International Symposium on Wearable Computers","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Minnen et al., 2006b] D. Minnen, T. Westeyn, T. Starner, J.A. Ward, and P. Lukowicz. Performance metrics and evaluation is- sues for continuous activity recognition. In Performance Metrics for Intelligent Systems, 2006.","title":"Performance metrics and evaluation issues for continuous activity recognition","authors":"Minnen","year":"2006","venue":"Performance Metrics for Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Oates, 2002] T. Oates. PERUSE: An unsupervised algorithm for finding recurring patterns in time series. In Int. Conf. on Data Mining, pages 330-337, 2002.","title":"PERUSE: An unsupervised algorithm for finding recurring patterns in time series","authors":"T Oates","year":"2002","venue":"Int. Conf. on Data Mining","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pavlovic et al., 2000] V. Pavlovic, J.M. Rehg, and J. MacCormick. Learning switching linear models of human motion. In NIPS, pages 981-987, 2000.","title":"Learning switching linear models of human motion","authors":"V Pavlovic, J M Rehg, J Maccormick","year":"2000","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ratanamahatana and Keogh, 2005] C.A. Ratanamahatana and E. Keogh. Three myths about dynamic time warping. In SIAM Int. Conf. on Data Mining, pages 506-510, 2005.","title":"Three myths about dynamic time warping","authors":"C A Ratanamahatana, E Keogh","year":"2005","venue":"SIAM Int. Conf. on Data Mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Starner et al., 1998] T. Starner, J. Weaver, and A.P. Pentland. Real- time american sign language recognition using desk and wearable computer based video. PAMI, 20(12):1371-1375, Dec. 1998.","title":"Realtime american sign language recognition using desk and wearable computer based video","authors":"T Starner, J Weaver, A P Pentland","year":"1998","venue":"PAMI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tanaka et al., 2005] Yoshiki Tanaka, Kazuhisa Iwamoto, and Ku- niaki Uehara. Discovery of time-series motif from multi- dimensional data based on mdl principle. Machine Learning, 58(2-3):269-300, 2005.","title":"Discovery of time-series motif from multidimensional data based on mdl principle","authors":"Yoshiki Tanaka, Kazuhisa Iwamoto, Kuniaki Uehara","year":"2005","venue":"Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}