{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T20:28:33Z","timestamp":1726000113409},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030043025"},{"type":"electronic","value":"9783030043032"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-04303-2_2","type":"book-chapter","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T10:42:01Z","timestamp":1542364921000},"page":"13-26","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features"],"prefix":"10.1007","author":[{"given":"Alejandro","family":"Catalina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos M.","family":"Ala\u00edz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 R.","family":"Dorronsoro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"2_CR1","unstructured":"Barbero, A., Sra, S.: Fast newton-type methods for total variation regularization. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 313\u2013320. Citeseer (2011)"},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183\u2013202 (2009)","journal-title":"SIAM J. Imaging Sci."},{"issue":"11","key":"2_CR3","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1016\/j.jspi.2013.05.019","volume":"143","author":"P B\u00fchlmann","year":"2013","unstructured":"B\u00fchlmann, P., R\u00fctimann, P., van de Geer, S., Zhang, C.H.: Correlated variables in regression: clustering and sparse estimation. J. Stat. Plan. Inference 143(11), 1835\u20131858 (2013)","journal-title":"J. Stat. Plan. Inference"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-319-71643-5_11","volume-title":"Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy","author":"A Catalina","year":"2017","unstructured":"Catalina, A., Dorronsoro, J.R.: NWP ensembles for wind energy uncertainty estimates. In: Woon, W.L., Aung, Z., Kramer, O., Madnick, S. (eds.) DARE 2017. LNCS (LNAI), vol. 10691, pp. 121\u2013132. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71643-5_11"},{"issue":"11","key":"2_CR5","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/LSP.2013.2278339","volume":"20","author":"L Condat","year":"2013","unstructured":"Condat, L.: A direct algorithm for 1-D total variation denoising. IEEE Signal Process. Lett. 20(11), 1054\u20131057 (2013)","journal-title":"IEEE Signal Process. Lett."},{"key":"2_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/978-3-319-19258-1_36","volume-title":"Advances in Computational Intelligence","author":"D D\u00edaz","year":"2015","unstructured":"D\u00edaz, D., Torres, A., Dorronsoro, J.R.: Deep neural networks for wind energy prediction. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9094, pp. 430\u2013443. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19258-1_36"},{"key":"2_CR7","unstructured":"Figueiredo, M., Nowak, R.: Ordered weighted l1 regularized regression with strongly correlated covariates: theoretical aspects. In: Artificial Intelligence and Statistics, pp. 930\u2013938 (2016)"},{"key":"2_CR8","unstructured":"Grave, E., Obozinski, G., Bach, F.: Trace lasso: a trace norm regularization for correlated designs. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS 2011. pp. 2187\u20132195 (2011)"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Hallac, D., Leskovec, J., Boyd, S.: Network lasso: clustering and optimization in large graphs. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 387\u2013396 (2015)","DOI":"10.1145\/2783258.2783313"},{"key":"2_CR10","unstructured":"Hernan Madrid Padilla, O., Scott, J.G., Sharpnack, J., Tibshirani, R.J.: The DFS Fused Lasso: Linear-Time Denoising over General Graphs. ArXiv e-prints, August 2016"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., Raskutti, G., Willett, R.: Graph-based regularization for regression problems with highly-correlated designs. ArXiv e-prints, March 2018","DOI":"10.1109\/GlobalSIP.2018.8646615"},{"key":"2_CR12","unstructured":"Lorbert, A., Eis, D., Kostina, V., Blei, D., Ramadge, P.: Exploiting covariate similarity in sparse regression via the pairwise elastic net. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, PMLR, Chia Laguna Resort, Sardinia, Italy, vol. 9, pp. 477\u2013484, 13\u201315 May 2010"},{"issue":"1","key":"2_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1111\/j.1467-9868.2005.00490.x","volume":"67","author":"R Tibshirani","year":"2005","unstructured":"Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., Knight, K.: Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(1), 91\u2013108 (2005)","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"issue":"1","key":"2_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 68(1), 49\u201367 (2006)","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"issue":"2","key":"2_CR15","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301\u2013320 (2005)","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"}],"container-title":["Lecture Notes in Computer Science","Data Analytics for Renewable Energy Integration. Technologies, Systems and Society"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04303-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,2]],"date-time":"2019-11-02T22:04:25Z","timestamp":1572732265000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-04303-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030043025","9783030043032"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04303-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"DARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Data Analytics for Renewable Energy Integration","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dare2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/workshops\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}