{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:32:06Z","timestamp":1740123126680,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8-9","license":[{"start":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T00:00:00Z","timestamp":1558396800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T00:00:00Z","timestamp":1558396800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s10994-019-05812-3","type":"journal-article","created":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T15:22:47Z","timestamp":1558538567000},"page":"1307-1327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LSALSA: accelerated source separation via learned sparse coding"],"prefix":"10.1007","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0959-8881","authenticated-orcid":false,"given":"Benjamin","family":"Cowen","sequence":"first","affiliation":[]},{"given":"Apoorva Nandini","family":"Saridena","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Choromanska","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,21]]},"reference":[{"key":"5812_CR1","unstructured":"Adler, J., & \u00d6ktem, O. (2017). Learned primal-dual reconstruction. CoRR arXiv:1707.06474 ."},{"issue":"9","key":"5812_CR2","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1109\/TIP.2010.2047910","volume":"19","author":"M Afonso","year":"2010","unstructured":"Afonso, M., Bioucas-Dias, J., & Figueiredo, M. (2010). Fast image recovery using variable splitting and constrained optimization. IEEE Transactions on Image Processing, 19(9), 2345\u20132356.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"5812_CR3","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","volume":"20","author":"M Afonso","year":"2011","unstructured":"Afonso, M., Bioucas-Dias, J., & Figueiredo, M. (2011). An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Transactions on Image Processing, 20(3), 681\u2013695.","journal-title":"IEEE Transactions on Image Processing"},{"key":"5812_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9467-7","volume-title":"Convex analysis and monotone operator theory in Hilbert spaces","author":"HH Bauschke","year":"2011","unstructured":"Bauschke, H. H., & Combettes, P. L. (2011). Convex analysis and monotone operator theory in Hilbert spaces (1st ed.). Berlin: Springer.","edition":"1"},{"issue":"1","key":"5812_CR5","first-page":"183","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM: SIAM Journal on Imaging Sciences, 2(1), 183\u2013202.","journal-title":"SIAM: SIAM Journal on Imaging Sciences"},{"key":"5812_CR6","doi-asserted-by":"crossref","unstructured":"Borgerding, M., & Schniter, P. (2016) Onsager-corrected deep learning for sparse linear inverse problems. In GlobalSIP.","DOI":"10.1109\/GlobalSIP.2016.7905837"},{"issue":"1","key":"5812_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends\u00ae in Machine Learning, 3(1), 1\u2013122.","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"5812_CR8","unstructured":"Chen, X., Liu, J., Wang, Z., & Yin, W. (2018). Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds. arXiv preprint arXiv:1808.10038 ."},{"key":"5812_CR9","unstructured":"Choromanska, A., Cowen, B., Kumaravel, S., Luss, R., Rish, I., Kingsbury, B., Tejwani, R., & Bouneffouf, D. (2019). Beyond backprop: Alternating minimization with co-activation memory. arXiv preprint arXiv:1806.09077v3 ."},{"issue":"11","key":"5812_CR10","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1002\/cpa.20042","volume":"57","author":"I Daubechies","year":"2004","unstructured":"Daubechies, I., Defrise, M., & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57(11), 1413\u20131457.","journal-title":"Communications on Pure and Applied Mathematics"},{"key":"5812_CR11","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/BF01581204","volume":"55","author":"J Eckstein","year":"1992","unstructured":"Eckstein, J., & Bertsekas, D. (1992). On the Douglas\u2013Rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming, 55, 293\u2013318.","journal-title":"Mathematical Programming"},{"issue":"3","key":"5812_CR12","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.acha.2005.03.005","volume":"19","author":"M Elad","year":"2005","unstructured":"Elad, M., Starck, J. L., Querre, P., & Donoho, D. L. (2005). Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA). Applied and Computational Harmonic Analysis, 19(3), 340\u2013358.","journal-title":"Applied and Computational Harmonic Analysis"},{"key":"5812_CR13","unstructured":"Elson, J., Douceur, J., Howell, J., & Saul, J. (2007). Asirra: A CAPTCHA that exploits interest-aligned manual image categorization. In ACM CCS."},{"key":"5812_CR14","doi-asserted-by":"crossref","unstructured":"Figueiredo, M., Bioucas-Dias, J., & Afonso, M. (2009). Fast frame-based image deconvolution using variable splitting and constrained optimization. In Proceedings of IEEE workshop on statistical signal processing (pp. 109\u2013112).","DOI":"10.1109\/SSP.2009.5278628"},{"key":"5812_CR15","first-page":"115","volume":"3","author":"FA Gers","year":"2003","unstructured":"Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2003). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, 115\u2013143.","journal-title":"Journal of Machine Learning Research"},{"key":"5812_CR16","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1137\/120896219","volume":"7","author":"T Goldstein","year":"2014","unstructured":"Goldstein, T., O\u2019Donoghue, B., & Setzer, S. (2014). Fast alternating direction optimization methods. SIAM Journal on Imaging Sciences, 7, 1588\u20131623.","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"5812_CR17","doi-asserted-by":"crossref","unstructured":"Golle, P. (2008). Machine learning attacks against the Asirra CAPTCHA. In ACM CCS.","DOI":"10.1145\/1455770.1455838"},{"key":"5812_CR18","unstructured":"Greff, K., Srivastava, R. K., & Schmidhuber, J. (2016). Highway and residual networks learn unrolled iterative estimation. arXiv preprint arXiv:1612.07771 ."},{"key":"5812_CR19","unstructured":"Gregor, K., & LeCun, Y. (2010). Learning fast approximations of sparse coding. In ICML."},{"key":"5812_CR20","doi-asserted-by":"crossref","unstructured":"Jarrett, K., Kavukcuoglu, K., Koray, M., & LeCun, Y. (2009). What is the best multi-stage architecture for object recognition? In ICCV.","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"5812_CR21","unstructured":"Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2010). Fast inference in sparse coding algorithms with applications to object recognition. CoRR arXiv:1010.3467 ."},{"key":"5812_CR22","unstructured":"Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images (Vol. 1, No. 4, p. 7). Technical report, University of Toronto."},{"key":"5812_CR23","unstructured":"Lange, M., Z\u00fchlke, D., Holz, O., Villmann, T. (2014). Applications of LP-norms and their smooth approximations for gradient based learning vector quantization. In ESANN."},{"key":"5812_CR24","unstructured":"Le Roux, J., Hershey, J. R., & Weninger, F. (2015). Deep NMF for speech separation. In ICASSP."},{"key":"5812_CR25","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (2009). Gradient-based learning applied to document recognition. In Proceedings of the IEEE."},{"key":"5812_CR26","unstructured":"Liao, Q., & Poggio, T. (2016). Bridging the gaps between residual learning, recurrent neural networks and visual cortex. arXiv preprint arXiv:1604.03640 ."},{"issue":"99","key":"5812_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JAS.2017.7510427","volume":"PP","author":"S Liu","year":"2017","unstructured":"Liu, S., Xian, Y., Li, H., & Yu, Z. (2017). Text detection in natural scene images using morphological component analysis and laplacian dictionary. IEEE\/CAA Journal of Automatica Sinica, PP(99), 1\u20139.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"5812_CR28","unstructured":"Moreau, T., & Bruna, J. (2016). Understanding trainable sparse coding with matrix factorization. arXiv preprint arXiv:1609.00285 ."},{"key":"5812_CR29","volume-title":"Introductory lectures on convex optimization: A basic course","author":"Y Nesterov","year":"2013","unstructured":"Nesterov, Y. (2013). Introductory lectures on convex optimization: A basic course (Vol. 87). Berlin: Springer."},{"key":"5812_CR30","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/381607a0","volume":"381","author":"B Olshausen","year":"1996","unstructured":"Olshausen, B., & Field, D. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607\u2013609.","journal-title":"Nature"},{"key":"5812_CR31","unstructured":"Orhan, E., & Pitkow, X. (2018). Skip connections eliminate singularities. In International conference on learning representations."},{"issue":"3","key":"5812_CR32","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1002\/mrm.25240","volume":"73","author":"R Otazo","year":"2015","unstructured":"Otazo, R., Cand\u00e8s, E., & Sodickson, D. K. (2015). Low-rank and sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic Resonance in Medicine, 73(3), 1125\u201336.","journal-title":"Magnetic Resonance in Medicine"},{"key":"5812_CR33","doi-asserted-by":"crossref","unstructured":"Parekh, A., Selesnick, I., Rapoport, D., & Ayappa, I. (2014). Sleep spindle detection using time-frequency sparsity. In IEEE SPMB.","DOI":"10.1109\/SPMB.2014.7002965"},{"key":"5812_CR34","doi-asserted-by":"crossref","unstructured":"Peyr\u00e9, G., Fadili, J., & Starck, J. L. (2007). Learning adapted dictionaries for geometry and texture separation. In SPIE Wavelets.","DOI":"10.1117\/12.731244"},{"issue":"3","key":"5812_CR35","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1137\/090770783","volume":"3","author":"G Peyr\u00e9","year":"2010","unstructured":"Peyr\u00e9, G., Fadili, J., & Starck, J. L. (2010). Learning the morphological diversity. SIAM Journal on Imaging Sciences, 3(3), 646\u2013669.","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"5812_CR36","doi-asserted-by":"crossref","unstructured":"Schmidt, M., Fung, G., & Rosales, R. (2007). Fast optimization methods for l1 regularization: A comparative study and two new approaches. In J. N. Kok, J.\u00a0Koronacki, R. L. D. Mantaras, S.\u00a0Matwin, D.\u00a0Mladeni\u010d, A.\u00a0Skowron (Eds.), ECML.","DOI":"10.1007\/978-3-540-74958-5_28"},{"key":"5812_CR37","unstructured":"Selesnick, I. (2014). L1-norm penalized least squares with salsa. Connexions (p. 66). Retrieved March 1, 2017 from http:\/\/cnx.org\/contents\/e980d3cd-f201-4ef6-8992-d712bf0a88a3@5 ."},{"key":"5812_CR38","doi-asserted-by":"crossref","unstructured":"Shoham, N., & Elad, M. (2008). Algorithms for signal separation exploiting sparse representations, with application to texture image separation. In Proceedings of the IEEE 25th convention of electrical and electronics engineers in Israel.","DOI":"10.1109\/EEEI.2008.4736587"},{"key":"5812_CR39","unstructured":"Sprechmann, P., Litman, R., Yakar, T., Bronstein, A., & Sapiro, G. (2013). Efficient supervised sparse analysis and synthesis operators. In NIPS."},{"key":"5812_CR40","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/S1076-5670(04)32006-9","volume":"132","author":"JL Starck","year":"2004","unstructured":"Starck, J. L., Elad, M., & Donoho, D. (2004). Redundant multiscale transforms and their application for morphological component separation. Advances in Imaging and Electron Physics, 132, 287\u2013348.","journal-title":"Advances in Imaging and Electron Physics"},{"issue":"10","key":"5812_CR41","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1109\/TIP.2005.852206","volume":"14","author":"JL Starck","year":"2005","unstructured":"Starck, J. L., Elad, M., & Donoho, D. (2005a). Image decomposition via the combination of sparse representations and a variational approach. IEEE Transactions on Image Processing, 14(10), 1570\u20131582.","journal-title":"IEEE Transactions on Image Processing"},{"key":"5812_CR42","doi-asserted-by":"crossref","unstructured":"Starck, J. L., Moudden, Y., Bobina, J., Elad, M., Donoho, D. (2005b). Morphological component analysis. In Proceedings of SPIE Wavelets.","DOI":"10.1117\/12.615237"},{"key":"5812_CR43","doi-asserted-by":"crossref","unstructured":"Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., & Lim\u00a0Tan, C. (2015). Text flow: A unified text detection system in natural scene images. In Proceedings of the IEEE international conference on computer vision (pp. 4651\u20134659).","DOI":"10.1109\/ICCV.2015.528"},{"issue":"5","key":"5812_CR44","doi-asserted-by":"publisher","first-page":"2925","DOI":"10.1109\/TGRS.2015.2508380","volume":"54","author":"F Uysal","year":"2016","unstructured":"Uysal, F., Selesnick, I., & Isom, B. (2016). Mitigation of wind turbine clutter for weather radar by signal separation. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2925\u20132934.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"1","key":"5812_CR45","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10915-018-0757-z","volume":"78","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Yin, W., & Zeng, J. (2019). Global convergence of ADMM in nonconvex nonsmooth optimization. Journal of Scientific Computing, 78(1), 29\u201363. https:\/\/doi.org\/10.1007\/s10915-018-0757-z .","journal-title":"Journal of Scientific Computing"},{"key":"5812_CR46","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ling, Q., & Huang, T. (2016). Learning deep L0 encoders. In AAAI.","DOI":"10.1609\/aaai.v30i1.10198"},{"key":"5812_CR47","doi-asserted-by":"crossref","unstructured":"Wisdom, S., Powers, T., Pitton, J., & Atlas, L. (2017). Deep recurrent NMF for speech separation by unfolding iterative thresholding. In IEEE workshop on applications of signal processing to audio and acoustics (WASPAA) (pp. 254\u2013258).","DOI":"10.1109\/WASPAA.2017.8170034"},{"key":"5812_CR48","unstructured":"Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. CoRR arXiv:1708.07747 ."},{"issue":"1","key":"5812_CR49","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TITS.2017.2749977","volume":"19","author":"C Yan","year":"2018","unstructured":"Yan, C., Xie, H., Liu, S., Yin, J., Zhang, Y., & Dai, Q. (2018). Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Transactions on Intelligent Transportation Systems, 19(1), 220\u2013229.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"5812_CR50","unstructured":"Yang, Y., Sun, J., Li, H., & Xu, Z. (2016). Deep ADMM-net for compressive sensing MRI. In NIPS."},{"key":"5812_CR51","doi-asserted-by":"crossref","unstructured":"Zhou, J., Di, K., Du, J., Peng, X., Yang, H., Pan, S.J., Tsang, I. W., Liu, Y., Qin, Z., & Goh, R. (2018). SC2Net: Sparse LSTMs for sparse coding. In AAAI.","DOI":"10.1609\/aaai.v32i1.11721"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-019-05812-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-019-05812-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-019-05812-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T12:09:57Z","timestamp":1663502997000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-019-05812-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,21]]},"references-count":51,"journal-issue":{"issue":"8-9","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["5812"],"URL":"https:\/\/doi.org\/10.1007\/s10994-019-05812-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2019,5,21]]},"assertion":[{"value":"22 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}