Invariant time-series factorization

Data Mining and Knowledge Discovery

Volumn 28, Issue 5-6, 2014, Pages 1455-1479

  Grabocka, Josif ^a   Schmidt Thieme, Lars ^a  

^a Information Systems and Machine Learning Lab ^*  (Germany)

Author keywords

Data mining; Time series classification; Time series factorization

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; STOCHASTIC SYSTEMS;

CLASSIFICATION ACCURACY; COORDINATE DESCENT; EXPERIMENTAL COMPARISON; FEATURE REPRESENTATION; OBJECTIVE FUNCTIONS; PREDICTION ACCURACY; REAL LIFE DATASETS; TIME SERIES CLASSIFICATIONS;

FACTORIZATION;

EID: 84937519690     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-014-0364-z     Document Type: Article

References (35)

1. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. SODA '07, Philadelphia, PA, society for industrial and applied mathematics, pp 1027-1035
2. Barthelemy Q, Larue A, Mayoue A, Mercier D, Mars J (2012) Shift and 2d rotation invariant sparse coding for multivariate signals. IEEE Trans Signal Process 60(4):1597-1611
3. Batista GEAPA, Wang X, Keogh EJ (2011) A complexity-invariant distance measure for time series. In: SDM, SIAM / Omnipress, pp 699-710
4. Batista GEAPA, Keogh EJ, Tataw OM, de Souza VMA (2014) CID: an efficient complexity-invariant distance for time series. Data Min Knowl Disc 28(3):634-669
5. Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. IEEE Trans Pattern Anal Mach Intell 35(11):2796-2802
6. Buza K, Schmidt-Thieme L (2010) Motif-based classification of time series with Bayesian networks and SVMs. In: Fink A, Lausen B, Seidel W, Ultsch A (eds) Advances in data analysis, data handling and business intelligence. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg, pp 105-114
7. Chen Y, Nascimento M, Ooi BC, Tung A (2007) Spade: on shape-based pattern detection in streaming time series. In: IEEE 23rd international conference on data engineering, 2007. ICDE 2007. pp 786-795
8. Chen L, Ng R (2004) On the marriage of lp-norms and edit distance. In: Proceedings of the thirtieth international conference on very large data bases - vol 30. VLDB '04, VLDB endowment, pp 792-803
9. Cuturi M (June 2011) Fast global alignment kernels. In: et al. G. (ed) Proceedings of the ICML 2011. ICML 2011, New York, ACM, pp 929-936
10. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1-30
11. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh EJ (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2):1542-1552
12. Grabocka J, Nanopoulos A, Schmidt-Thieme L (2012a) Classification of sparse time series via supervised matrix factorization. In: Hoffmann J, Selman B (eds) AAAI, AAAI Press
13. Grabocka J, Nanopoulos A, Schmidt-Thieme L (2012b) Invariant time-series classification. In: Flach PA, Bie TD, Cristianini N (eds) ECML/PKDD (2). Lecture notes in computer science, vol 7524. Springer, pp 725-740
14. Gudmundsson S, Runarsson TP, Sigurdsson S (2008) Support vector machines and dynamic time warping for time series. In: IJCNN, IEEE, pp 2772-2776
15. Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2013) Classification of time series by shapelet transformation. Data Min Knowl Disc 28:851-881
16. Huang PS, Yang J, Hasegawa-Johnson M, Liang F, Huang TS (2012) Pooling robust shift-invariant sparse representations of acoustic signals. In: INTERSPEECH, ISCA
17. Keogh EJ, Pazzani MJ (2000) Scaling up dynamic time warping for datamining applications. In: KDD. pp 285-289
18. Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263-286
19. Kuksa P, Pavlovic V (2010) Spatial representation for efficient sequence classification. In: 20th international conference on pattern recognition (ICPR), 2010. pp 3320-3323
20. Lewicki MS, Sejnowski TJ (1999) Coding time-varying signals using sparse, shift-invariant representations. In: Proceedings of NIPS, Cambridge, MIT Press pp 730-736
21. Lin J, Khade R, Li Y (2012) Rotation-invariant similarity in time series using bag-of-patterns representation. J Intell Inf Syst 39(2):287-315
22. Lin J, Li Y (2009) Finding structural similarity in time series data using bag-of-patterns representation. In: Proceedings of the 21st international conference on scientific and statistical database management. SSDBM 2009, Springer, Berlin pp 461-477
23. Lin J, Keogh E, Wei L, Lonardi S (October 2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Disc 15(2):107-144
24. Marussy K, Buza K (2013) Success: a new approach for semi-supervised classification of time-series. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (eds) Artificial intelligence and soft computing. Lecture notes in computer science, vol 7894. Springer, Berlin, pp 437-447
25. Mueen A, Keogh EJ, Young N. (2011) Logical-shapelets: an expressive primitive for time series classification. In: Apté C, Ghosh J, Smyth P (eds) KDD, ACM, pp 1154-1162
26. Platt JC (1999) Advances in kernel methods. MIT Press, Cambridge
27. Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E (2012) Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD. KDD 2012, New York, ACM, pp 262-270
28. Rakthanmanon T, Keogh E (2013) Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 13th SIAM international conference on data mining
29. Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering, 2002. pp 673-684
30. Wang X, Mueen A, Ding H, Trajcevski G, Scheuermann P, Keogh E (2013) Experimental comparison of representation methods and distance measures for time series data. Data Min Knowl Disc 26(2):275-309
31. Wang J, Liu P, She MF, Nahavandi S, Kouzani A (2013) Bag-of-words representation for biomedical time series classification. Biomed Signal Process Control 8(6):634-644
32. Wang F, Lee N, Hu J, Sun J, Ebadollahi S (2012) Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach. In: Proceedings of ACM SIGKDD. KDD '12, New York, ACM pp 453-461
33. Wei L, Keogh E (2006) Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. KDD '06, New York, ACM pp 748-753
34. Yu HF, Hsieh CJ, Si S, Dhillon IS (2012) Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In: Zaki MJ, Siebes A, Yu JX, Goethals B, Webb GI, Wu X (eds) ICDM, IEEE computer society, pp 765-774
35. Zhang D, Zuo W, Zhang D, Zhang H (2010) Time series classification using support vector machine with Gaussian elastic metric kernel. In: ICPR, IEEE, pp 29-32