Decomposing Activities of Daily Living to Discover Routine Clusters
暂无分享,去创建一个
Pearl Pu | Onur Yürüten | Jiyong Zhang | P. Pu | Jiyong Zhang | O. Yürüten
[1] L. Smarr. Quantifying your body: a how-to guide from a systems biology perspective. , 2012, Biotechnology journal.
[2] Dacheng Tao,et al. GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case , 2011, ICML.
[3] Majid Sarrafzadeh,et al. Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series , 2009, IJCAI.
[4] O. Okonkwo,et al. Mild cognitive impairment and everyday function: evidence of reduced speed in performing instrumental activities of daily living. , 2008, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.
[5] Tak-Chung Fu,et al. A review on time series data mining , 2011, Eng. Appl. Artif. Intell..
[6] John Wright,et al. Decomposing background topics from keywords by principal component pursuit , 2010, CIKM.
[7] G. K. Sandve,et al. A survey of motif discovery methods in an integrated framework , 2006, Biology Direct.
[8] John Wright,et al. RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images , 2012, IEEE Trans. Pattern Anal. Mach. Intell..
[9] Pearl Pu,et al. HealthyTogether: exploring social incentives for mobile fitness applications , 2014, Chinese CHI '14.
[10] Patrick Olivier,et al. A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation , 2010, AmI.
[11] Frank Bentley,et al. Mobile Health Mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.
[12] E. Prescott,et al. Postwar U.S. Business Cycles: An Empirical Investigation , 1997 .
[13] G. Sapiro,et al. A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.
[14] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[15] Lawrence B. Holder,et al. Discovering Activities to Recognize and Track in a Smart Environment , 2011, IEEE Transactions on Knowledge and Data Engineering.
[16] Alex Pentland,et al. Social sensing for epidemiological behavior change , 2010, UbiComp.
[17] Mong-Li Lee,et al. Integrating Frequent Pattern Mining from Multiple Data Domains for Classification , 2012, 2012 IEEE 28th International Conference on Data Engineering.
[18] Yi Ma,et al. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.
[19] Ling Bao,et al. Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.
[20] R. Manmatha,et al. Word image matching using dynamic time warping , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[21] Volkan Cevher,et al. MATRIX ALPS: Accelerated low rank and sparse matrix reconstruction , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).
[22] Ali S. Hadi,et al. Finding Groups in Data: An Introduction to Chster Analysis , 1991 .
[23] Eamonn J. Keogh,et al. Scaling up Dynamic Time Warping to Massive Dataset , 1999, PKDD.
[24] B. Lo,et al. Pattern mining for routine behaviour discovery in pervasive healthcare environments , 2008, 2008 International Conference on Information Technology and Applications in Biomedicine.
[25] Yi Ma,et al. Repairing Sparse Low-Rank Texture , 2012, ECCV.
[26] Weng-Keen Wong,et al. Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method , 2013, IAAI.
[27] S. Chiba,et al. Dynamic programming algorithm optimization for spoken word recognition , 1978 .
[28] Donald J. Berndt,et al. Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.
[29] Lionel M. Ni,et al. An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data , 2012, UbiComp.
[30] Diane J. Cook,et al. Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.
[31] Eamonn J. Keogh,et al. Clustering of time-series subsequences is meaningless: implications for previous and future research , 2004, Knowledge and Information Systems.
[32] Eamonn J. Keogh,et al. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.