A. and A. A. Frank, UCI machine learning repository, 2010.

R. Agrawal, Christos Faloutsos & Arun Swami. Efficient similarity search in sequence databases, International Conference on Foundations of Data Organization and Algorithms, pp.69-84, 1993.

, SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on, vol.54, issue.11, pp.4311-4322, 2006.

M. Elad, Sparse and redundant modeling of image content using an image-signature-dictionary, SIAM Journal on Imaging Sciences, vol.1, issue.3, pp.228-247, 2008.

&. George-b-arfken, J. Hans, and . Weber, Mathematical methods for physicists, 1999.

Q. Barthélemy, A. Larue, A. Mayoue, D. Mercier, &. Jérôme et al., Shift & 2D rotation invariant sparse coding for multivariate signals, IEEE Transactions on, vol.60, issue.4, pp.1597-1611, 2012.

S. Bengio and F. Pereira, Yoram Singer & Dennis Strelow. Group sparse coding, Advances in neural information processing systems, pp.82-89, 2009.

E. P. George, . Box, M. Gwilym, G. C. Jenkins, &. Reinsel et al., Time series analysis: forecasting and control, 2015.

S. Paul, L. Bradley-&-olvi, and . Mangasarian, K-plane clustering, Journal of Global Optimization, vol.16, issue.1, pp.23-32, 2000.

H. Bristow, A. Eriksson-&-simon, and . Lucey, Fast convolutional sparse coding, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.391-398, 2013.

R. Bro, &. Tamara, and G. Kolda, Resolving the sign ambiguity in the singular value decomposition, Journal of Chemometrics, vol.22, issue.2, pp.135-140, 2008.

O. Bryt and &. Michael-elad, Compression of facial images using the K-SVD algorithm, Journal of Visual Communication and Image Representation, vol.19, issue.4, pp.270-282, 2008.

E. David and L. Donoho, Curvelets: A surprisingly effective nonadaptive representation for objects with edges, 2000.

K. Fu, Efficient time series matching by wavelets, Proceedings., 15th International Conference on, pp.126-133, 1999.

D. L. Scott-shaobing-chen, &. Donoho, A. Michael, and . Saunders, Atomic decomposition by basis pursuit, SIAM review, vol.43, issue.1, pp.129-159, 2001.

M. Chen, Ghassan AlRegib & Biing-Hwang Juang. 6dmg: A new 6d motion gesture database, Proceedings of the 3rd

, Multimedia Systems Conference, pp.83-88, 2012.

Z. Chen, W. Zuo, and Q. Hu-&-liang-lin, Kernel sparse representation for time series classification, Information Sciences, vol.292, pp.15-26, 2015.

C. Chu, Time series segmentation: A sliding window approach, Information Sciences, vol.85, issue.1-3, pp.147-173, 1995.

S. Chu, S. Narayanan, and &. Kuo, Environmental sound recognition with time-frequency audio features, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.6, pp.1142-1158, 2009.

M. Cuturi, Fast global alignment kernels, Proceedings of the 28th international conference on machine learning (ICML-11), pp.929-936, 2011.

C. Do, A. Douzal-chouakria, and S. Marié, Michèle Rombaut & Saeed Varasteh. Multi-modal and multi-scale temporal metric learning for a robust time series nearest neighbors classification, Information Sciences, vol.418, pp.272-285, 2017.

M. Elad-&-michal and A. , Image denoising via sparse and redundant representations over learned dictionaries, IEEE Transactions on Image processing, vol.15, issue.12, pp.3736-3745, 2006.

E. Elhamifar and &. Vidal, Sparse subspace clustering: Algorithm, theory, and applications, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.2765-2781, 2013.

K. Engan, S. O. Aase, and &. Husoy, Method of optimal directions for frame design, Acoustics, Speech, and Signal Processing, vol.5, pp.2443-2446, 1999.

C. Principe, Competitive principal component analysis for locally stationary time series, IEEE Transactions on Signal Processing, vol.46, issue.11, pp.3068-3081, 1998.

T. Fu and F. Ng, Kernel sparse representation for image classification and face recognition, European Conference on Computer Vision, pp.1-14, 2006.

. Springer, , 2010.

L. Ary, . Goldberger, A. N. Luis, L. Amaral, . Glass et al.,

P. Physiobank and . Physionet, Circulation, vol.101, issue.23, pp.215-220, 2000.

R. Grosse, R. Raina, H. Kwong, &. Andrew, and Y. Ng, Shiftinvariant sparse coding for audio classification, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, pp.149-158, 2007.

T. Guha, &. Rabab, and K. Ward, Learning sparse representations for human action recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.8, pp.1576-1588, 2012.

V. Guralnik and &. Srivastava, Event detection from time series data, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.33-42, 1999.

J. Douglas and H. , Time series analysis, vol.2, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00700245

K. Huang-&-selin-aviyente, Sparse representation for signal classification, Advances in neural information processing systems, pp.609-616, 2006.

P. Huang, J. Yang, M. Hasegawa-johnson, F. Liang, &. Thomas et al., Pooling Robust Shift-Invariant Sparse Representations of Acoustic Signals, INTERSPEECH, vol.12, pp.2518-2521, 2012.

A. László, A. Jeni, Z. L?rincz, . Szabó, and . Jeffrey-f-cohn-&-takeo-kanade, Spatio-temporal event classification using time-series kernel based structured sparsity, Computer Vision-ECCV 2014, pp.135-150, 2014.

Z. Jiang, Z. Lin, &. Larry, and S. Davis, Learning a discriminative dictionary for sparse coding via label consistent K-SVD, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.1697-1704, 2011.

P. Jost, P. Vandergheynst, . Sylvain-lesage-&-rémi, and . Gribonval, MoTIF: An efficient algorithm for learning translation invariant dictionaries, Proceedings. 2006 IEEE International Conference on, vol.5, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544911

B. Hwang and . Laurence-r-rabiner, Hidden Markov models for speech recognition, Technometrics, vol.33, issue.3, pp.251-272, 1991.

K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu-&-yann et al., Learning convolutional feature hierarchies for visual recognition, Advances in neural information processing systems, pp.1090-1098, 2010.

E. Keogh, K. Chakrabarti, and M. Pazzani-&-sharad-mehrotra, Dimensionality reduction for fast similarity search in large time series databases, Knowledge and information Systems, vol.3, issue.3, pp.263-286, 2001.

E. Keogh, The UCR time series data mining archive, 2006.

J. Michael-s-lewicki-&-terrence and . Sejnowski, Coding time-varying signals using sparse, shift-invariant representations, Proceedings of the AAAI Conference on Artificial Intelligence, pp.2189-2195, 1999.

J. Lin, E. Keogh, and S. Chiu, A symbolic representation of time series, with implications for streaming algorithms, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pp.2-11, 2003.

H. Lütkepohl, New introduction to multiple time series analysis, 2005.

B. Mailhé, R. Sylvain-lesage, and . Gribonval, Frédéric Bimbot & Pierre Vandergheynst. Shift-invariant dictionary learning for sparse representations: extending K-SVD, Signal Processing Conference, pp.1-5, 2008.

B. Mailhé and &. Plumbley, Dictionary learning with large step gradient descent for sparse representations. Latent Variable Analysis and Signal Separation, pp.231-238, 2012.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro-&-andrew-zisserman, Discriminative learned dictionaries for local image analysis, Computer Vision and Pattern Recognition, pp.1-8, 2008.

J. Mairal, J. Ponce, G. Sapiro, A. Zisserman, &. Francis et al., Supervised dictionary learning, Advances in neural information processing systems, pp.1033-1040, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00322431

A. Majumdar, &. Rabab, and K. Ward, Fast group sparse classification, Canadian Journal of Electrical and Computer Engineering, vol.34, issue.4, pp.136-144, 2009.

G. Stéphane and . Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on, vol.41, issue.12, pp.3397-3415, 1993.

P. Nemenyi, Distribution-free multiple comparisons, Biometrics, vol.18, issue.2, p.263, 1962.

Y. Andrew, M. Ng, and . Weiss, On spectral clustering: Analysis and an algorithm, Advances in neural information processing systems, pp.849-856, 2002.

&. Bruno-a-olshausen, J. David, and . Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, p.607, 1996.

B. Ophir and M. Lustig-&-michael-elad, Multi-scale dictionary learning using wavelets, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.5, pp.1014-1024, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00700250

Y. Chandra-pati, R. Rezaiifar, and &. P. Sambamurthy-krishnaprasad, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Signals, Systems and Computers, Conference Record of The Twenty-Seventh Asilomar Conference on, pp.40-44, 1993.

D. Pham-&-svetha and . Venkatesh, Joint learning and dictionary construction for pattern recognition, Computer Vision and Pattern Recognition, pp.1-8, 2008.

S. Poularakis and G. Tsagkatakis, Panagiotis Tsakalides & Ioannis Katsavounidis. Sparse representations for hand gesture recognition, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3746-3750, 2013.

L. Juang, An introduction to hidden Markov models, ieee assp magazine, vol.3, issue.1, pp.4-16, 1986.

I. Ramírez, F. Lecumberry-&-guillermo, and . Sapiro, Sparse modeling with universal priors and learned incoherent dictionaries, University of Minnesota. Institute for Mathematics and Its Applications, 2009.

I. Ramirez, P. Sprechmann-&-guillermo, and . Sapiro, Classification and clustering via dictionary learning with structured incoherence and shared features, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp.3501-3508, 2010.

M. William and . Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical association, vol.66, issue.336, pp.846-850, 1971.

R. Rubinstein and A. M. Bruckstein-&-michael-elad, Dictionaries for sparse representation modeling, Proceedings of the IEEE, vol.98, pp.1045-1057, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00565811

H. Sakoe and &. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE transactions on acoustics, speech, and signal processing, vol.26, pp.43-49, 1978.

E. Smith, &. Michael, and S. Lewicki, Ahlame Douzal-Chouakria & Eric Gaussier. Generalized k-means-based clustering for temporal data under weighted and kernel time warp, Pattern Recognition Letters, vol.17, issue.1, pp.63-69, 2005.

T. Stiefmeier, D. Roggen-&-gerhard, and . Tröster, Gestures are strings: efficient online gesture spotting and classification using string matching, Proceedings of the ICST 2nd international conference on Body area networks, p.16, 2007.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

A. Joel and . Tropp, Greed is good: Algorithmic results for sparse approximation. Information Theory, IEEE Transactions on, vol.50, issue.10, pp.2231-2242, 2004.

P. Tseng, ;. Hien-van-nguyen, M. Vishal, . Patel, M. Nasser et al., Nearest q-flat to m points, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol.105, pp.2021-2024, 2000.

. Hien-van-nguyen, M. Vishal, . Patel, M. Nasser, and . Nasrabadi-&-rama-chellappa, Design of non-linear kernel dictionaries for object recognition, IEEE Transactions on Image Processing, vol.22, issue.12, pp.5123-5135, 2013.

J. Wang, J. Yang, K. Yu, and F. Lv, Thomas Huang & Yihong Gong. Locality-constrained linear coding for image classification, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp.3360-3367, 2010.

D. Wang and &. Kong, A classification-oriented dictionary learning model: Explicitly learning the particularity and commonality across categories, Pattern Recognition, vol.47, issue.2, pp.885-898, 2014.

C. Wei, Y. Chao, and Y. Wang, Locality-sensitive dictionary learning for sparse representation based classification, Pattern Recognition, vol.46, issue.5, pp.1277-1287, 2013.

C. Wei, Y. Chao, and Y. Wang, Locality-sensitive dictionary learning for sparse representation based classification, Pattern Recognition, vol.46, issue.5, pp.1277-1287, 2013.

J. Wright, Y. Allen, A. Yang, . Ganesh, S. Shankar et al., Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.2, pp.210-227, 2009.

M. Yang, L. Zhang, J. Yang-&-dejing, and . Zhang, Metaface learning for sparse representation based face recognition, 17th IEEE International Conference on, pp.1601-1604, 2010.

M. Yang and L. Zhang, Xiangchu Feng & David Zhang. Fisher discrimination dictionary learning for sparse representation, 2011 IEEE International Conference on, pp.543-550, 2011.

. Saeed-varasteh-yazdi-&-ahlame-douzal-chouakria, Time warp invariant kSVD: Sparse coding and dictionary learning for time series under time warp, Pattern Recognition Letters, vol.112, pp.1-8, 2018.

, Time warp invariant dictionary learning for time series clustering: application to music data stream analysis, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.356-372

. Springer, , 2018.

J. Yin, Z. Liu, J. Zhong, and . Yang, Kernel sparse representation based classification, Neurocomputing, vol.77, issue.1, pp.120-128, 2012.

C. You, D. Robinson-&-rené, and . Vidal, Scalable sparse subspace clustering by orthogonal matching pursuit, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3918-3927, 2016.

M. Yuan and &. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.49-67, 2006.

, A large margin time series nearest neighbour classification under locally weighted time warps, Knowledge and Information Systems, pp.1-19, 2018.

Q. Zhang and &. Li, Discriminative K-SVD for dictionary learning in face recognition, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp.2691-2698, 2010.

L. Zhang, W. Zhou, P. Chang, J. Liu, Z. Yan et al., Kernel sparse representation-based classifier, IEEE Transactions on Signal Processing, vol.60, issue.4, pp.1684-1695, 2012.

Z. Ognyan-ivanov, N-dimensional Rotation Matrix Generation Algorithm, American Journal of Computational and Applied Mathematics, vol.7, issue.2, pp.51-57, 2017.

Y. Zhou, K. Liu, R. E. Carrillo, K. E. Barner-&-fouad, and . Kiamilev, Kernel-based sparse representation for gesture Final version Printed on, 2019.

, Pattern Recognition, vol.46, issue.12, pp.3208-3222, 2013.