References
[1]. Hiroaki Sakoe and Seibi Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 26(1):43–49, 1978.
[2]. Claus Bahlmann and Hans Burkhardt. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):299–310, 2004.
[3]. Zsolt Miklos Kovacs-Vajna. A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1266–1276, 2000.
[4]. Samsu Sempena, Nur Ulfa Maulidevi, and Peb Ruswono Aryan. Human action recognition using dynamic time warping. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, pages 1–5. IEEE, 2011.
[5]. Philippe Esling and Carlos Agon. Time-series data mining. ACM Computing Surveys (CSUR), 45(1):12, 2012.
[6]. Tak-chung Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164–181, 2011.
[7]. Youngha Hwang and Saul B Gelfand. Constrained sparse dynamic time warping. In International Conference on Machine Learning and Applications, pages 216–222. IEEE, 2018.
[8]. Youngha Hwang and Saul B Gelfand. Fast sparse dynamic time warping. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 3872–3877. IEEE, 2022.
[9]. Heather A Eicher-Miller, Saul Gelfand, Youngha Hwang, Edward Delp, Anindya Bhadra, and Jiaqi Guo. Distance metrics optimized for clustering temporal dietary patterning among us adults. Appetite, 144:104451, 2020.
[10]. David Murray and L Stankovic. Refit: electrical load measurements. URL= http://www.refitsmarthomes.org/, 2017.
[11]. Youngha Hwang and Saul B Gelfand. Sparse dynamic time warping. In International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 163–175. Springer, 2017.
[12]. Youngha Hwang and Saul B Gelfand. Binary sparse dynamic time warping. In International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 748–759. Springer, 2019.
[13]. Youngha Hwang. Constrained binary sparse dynamic time warping. In manuscript.
[14]. Abdullah Mueen, Nikan Chavoshi, Noor Abu-El-Rub, Hossein Hamooni, Amanda Minnich, and Jonathan MacCarthy. Speeding up dynamic time warping distance for sparse time series data. Knowledge and Information Systems, 54(1):237–263, 2018.
[15]. Eamonn Keogh, Xiaopeng Xi, Li Wei, and Chotirat Ann Ratanamahatana. The ucr time series classification/clustering homepage. URL= http://www.cs.ucr.edu/˜ eamonn/time series data, 2006.
Cite this article
Hwang,Y. (2024). Fast Constrained Sparse Dynamic Time Warping. Applied and Computational Engineering,120,50-58.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Hiroaki Sakoe and Seibi Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 26(1):43–49, 1978.
[2]. Claus Bahlmann and Hans Burkhardt. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):299–310, 2004.
[3]. Zsolt Miklos Kovacs-Vajna. A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1266–1276, 2000.
[4]. Samsu Sempena, Nur Ulfa Maulidevi, and Peb Ruswono Aryan. Human action recognition using dynamic time warping. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, pages 1–5. IEEE, 2011.
[5]. Philippe Esling and Carlos Agon. Time-series data mining. ACM Computing Surveys (CSUR), 45(1):12, 2012.
[6]. Tak-chung Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164–181, 2011.
[7]. Youngha Hwang and Saul B Gelfand. Constrained sparse dynamic time warping. In International Conference on Machine Learning and Applications, pages 216–222. IEEE, 2018.
[8]. Youngha Hwang and Saul B Gelfand. Fast sparse dynamic time warping. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 3872–3877. IEEE, 2022.
[9]. Heather A Eicher-Miller, Saul Gelfand, Youngha Hwang, Edward Delp, Anindya Bhadra, and Jiaqi Guo. Distance metrics optimized for clustering temporal dietary patterning among us adults. Appetite, 144:104451, 2020.
[10]. David Murray and L Stankovic. Refit: electrical load measurements. URL= http://www.refitsmarthomes.org/, 2017.
[11]. Youngha Hwang and Saul B Gelfand. Sparse dynamic time warping. In International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 163–175. Springer, 2017.
[12]. Youngha Hwang and Saul B Gelfand. Binary sparse dynamic time warping. In International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 748–759. Springer, 2019.
[13]. Youngha Hwang. Constrained binary sparse dynamic time warping. In manuscript.
[14]. Abdullah Mueen, Nikan Chavoshi, Noor Abu-El-Rub, Hossein Hamooni, Amanda Minnich, and Jonathan MacCarthy. Speeding up dynamic time warping distance for sparse time series data. Knowledge and Information Systems, 54(1):237–263, 2018.
[15]. Eamonn Keogh, Xiaopeng Xi, Li Wei, and Chotirat Ann Ratanamahatana. The ucr time series classification/clustering homepage. URL= http://www.cs.ucr.edu/˜ eamonn/time series data, 2006.