{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:58:42Z","timestamp":1776880722459,"version":"3.51.2"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319535463","type":"print"},{"value":"9783319535470","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-53547-0_1","type":"book-chapter","created":{"date-parts":[[2017,2,14]],"date-time":"2017-02-14T09:54:19Z","timestamp":1487066059000},"page":"3-15","source":"Crossref","is-referenced-by-count":15,"title":["Higher-Order Block Term Decomposition for Spatially Folded fMRI Data"],"prefix":"10.1007","author":[{"given":"Christos","family":"Chatzichristos","sequence":"first","affiliation":[]},{"given":"Eleftherios","family":"Kofidis","sequence":"additional","affiliation":[]},{"given":"Yiannis","family":"Kopsinis","sequence":"additional","affiliation":[]},{"given":"Manuel Morante","family":"Moreno","sequence":"additional","affiliation":[]},{"given":"Sergios","family":"Theodoridis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,2,15]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1214\/09-STS282","volume":"23","author":"MA Lindquist","year":"2008","unstructured":"Lindquist, M.A.: The statistical analysis of fMRI data. Stat. Sci. 23, 439\u2013464 (2008)","journal-title":"Stat. Sci."},{"key":"1_CR2","volume-title":"Machine Learning: A Bayesian and Optimization Perspective","author":"S Theodoridis","year":"2015","unstructured":"Theodoridis, S.: Machine Learning: A Bayesian and Optimization Perspective. Academic Press, Boston (2015)"},{"key":"1_CR3","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1016\/j.neuroimage.2004.02.026","volume":"22","author":"AH Andersen","year":"2004","unstructured":"Andersen, A.H., Rayens, W.S.: Structure-seeking multilinear methods for the analysis of fMRI data. NeuroImage 22, 728\u2013739 (2004)","journal-title":"NeuroImage"},{"key":"1_CR4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MEMB.2006.1607672","volume":"25","author":"VD Calhoun","year":"2006","unstructured":"Calhoun, V.D., Adal\u0131, T.: Unmixing fMRI with independent component analysis. IEEE Eng. Med. Biol. Mag. 25, 79\u201390 (2006)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Andersen, K.W., M\u00f8rup, M., Siebner, H., Madsen, K.H., Hansen, L.K.: Identifying modular relations in complex brain networks. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (2012)","DOI":"10.1109\/MLSP.2012.6349739"},{"key":"1_CR6","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1002\/1099-128X(200005\/06)14:3<229::AID-CEM587>3.0.CO;2-N","volume":"14","author":"N Sidiropoulos","year":"2000","unstructured":"Sidiropoulos, N., Bro, R.: On the uniqueness of multilinear decomposition of N-way arrays. J. Chemom. 14, 229\u2013239 (2000)","journal-title":"J. Chemom."},{"key":"1_CR7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2013.2297439","volume":"32","author":"A Cichocki","year":"2015","unstructured":"Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., Phan, H.A.: Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process. Mag. 32, 145\u2013163 (2015)","journal-title":"IEEE Signal Process. Mag."},{"key":"1_CR8","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1137\/060661685","volume":"30","author":"L Lathauwer De","year":"2008","unstructured":"De Lathauwer, L.: Decompositions of a higher-order tensor in block terms-Part I: lemmas for partitioned matrices. SIAM J. Matrix Anal. Appl. 30, 1022\u20131032 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"1_CR9","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1137\/070690729","volume":"30","author":"L Lathauwer De","year":"2008","unstructured":"De Lathauwer, L.: Decompositions of a higher-order tensor in block terms-Part II: definitions and uniqueness. SIAM J. Matrix Anal. Appl. 30, 1033\u20131066 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"1_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-28551-6_1","volume-title":"Latent Variable Analysis and Signal Separation","author":"L Lathauwer","year":"2012","unstructured":"Lathauwer, L.: Block component analysis, a new concept for blind source separation. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds.) LVA\/ICA 2012. LNCS, vol. 7191, pp. 1\u20138. Springer, Heidelberg (2012). doi: 10.1007\/978-3-642-28551-6_1"},{"key":"1_CR11","unstructured":"Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an \u201cexplanatory\u201d multi-modal factor analysis. UCLA Work. Papers in Phonetics, pp. 1\u201384(1970)"},{"issue":"2","key":"1_CR12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0024-3795(77)90069-6","volume":"18","author":"JB Kruskal","year":"1977","unstructured":"Kruskal, J.B.: Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra Appl. 18(2), 95\u2013138 (1977)","journal-title":"Linear Algebra Appl."},{"key":"1_CR13","unstructured":"Stegeman, A.: Comparing Independent Component Analysis and the PARAFAC model for artificial multi-subject fMRI data. Unpublished Technical report, University of Groningen (2007)"},{"key":"1_CR14","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.jneumeth.2012.12.009","volume":"2","author":"NE Helwig","year":"2013","unstructured":"Helwig, N.E., Hong, S.: A critique of tensor probabilistic independent component analysis: implications and recommendations for multi-subject fmri data analysis. J. Neurosci. Methods 2, 263\u2013273 (2013)","journal-title":"J. Neurosci. Methods"},{"issue":"5","key":"1_CR15","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1002\/cem.801","volume":"17","author":"R Bro","year":"2003","unstructured":"Bro, R., Kiers, H.: A new efficient method for determining the number of components in PARAFAC models. J. Chemom. 17(5), 274\u2013286 (2003)","journal-title":"J. Chemom."},{"issue":"4","key":"1_CR16","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/S0168-9274(01)00179-9","volume":"43","author":"JL Castellanos","year":"2002","unstructured":"Castellanos, J.L., Gmez, S., Guerra, V.: The triangle method for finding the corner of the L-curve. Appl. Numer. Math. 43(4), 359\u2013373 (2002)","journal-title":"Appl. Numer. Math."},{"key":"1_CR17","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.neuroimage.2004.10.043","volume":"25","author":"C Beckmann","year":"2005","unstructured":"Beckmann, C., Smith, S.: Tensorial extensions of independent component analysis for multisubject fMRI analysis. NeuroImage 25, 294\u2013311 (2005)","journal-title":"NeuroImage"},{"issue":"2","key":"1_CR18","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TMI.2003.822821","volume":"23","author":"C Beckmann","year":"2004","unstructured":"Beckmann, C., Smith, S.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137\u2013152 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"19","key":"1_CR19","doi-asserted-by":"crossref","first-page":"4847","DOI":"10.1109\/TSP.2013.2269046","volume":"61","author":"AH Phan","year":"2013","unstructured":"Phan, A.H., Tichavsky, P., Cichocki, A.: CANDECOMP\/PARAFAC decomposition of high-order tensors through tensor reshaping. IEEE Trans. Signal Process. 61(19), 4847\u20134860 (2013)","journal-title":"IEEE Trans. Signal Process."},{"issue":"8","key":"1_CR20","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/TSP.2013.2245660","volume":"61","author":"P Tichavsky","year":"2013","unstructured":"Tichavsky, P., Phan, A.H., Koldovsky, Z.: Cram\u00e9r-Rao-induced bounds for CANDECOMP\/PARAFAC tensor decomposition. IEEE Trans. Signal Process. 61(8), 1986\u20131997 (2013)","journal-title":"IEEE Trans. Signal Process."},{"issue":"11","key":"1_CR21","first-page":"970","volume":"120","author":"L Norgaard","year":"1995","unstructured":"Norgaard, L.: Classification and prediction of quality and process parameters of thick juice and beet sugar by fluorescence spectroscopy and chemometrics. Zuckerindustrie 120(11), 970\u2013981 (1995)","journal-title":"Zuckerindustrie"},{"key":"1_CR22","unstructured":"Phillips, N.C.: Gasthuisberg University Hospital raises fMRI to new level with Intera 3.0 T. http:\/\/netforum.healthcare.philips.com\/"},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Hunyadi, B., Camps, D., Sorber, L., Van Paesschen, W., De Vos, M., Van Huffel, S., De Lathauwer, L.: Block term decomposition for modelling epileptic seizures. EURASIP J. Adv. Signal Process. (2014). doi: 10.1186\/1687-6180-2014-139","DOI":"10.1186\/1687-6180-2014-139"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Phan, A.H., Cichocki, A., Zdunek, R., Lehky, S.: From basis components to complex structural patterns. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver (2013)","DOI":"10.1109\/ICASSP.2013.6638254"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Brie, D., Miron, S., Caland, F., Mustin, C.: An uniqueness condition for the 4-way CANDECOMP\/PARAFAC model with collinear loadings in three modes. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague (2011)","DOI":"10.1109\/ICASSP.2011.5947256"},{"key":"1_CR26","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/JSTSP.2015.2400415","volume":"9","author":"L Sorber","year":"2015","unstructured":"Sorber, L., Barel, M.V., De Lathauwer, L.: Structured data fusion. IEEE J. Sel. Topics Signal Process. 9, 586\u2013600 (2015)","journal-title":"IEEE J. Sel. Topics Signal Process."},{"key":"1_CR27","unstructured":"Vervliet, N., Debals, O., Sorber, L., Van Barel, M., De Lathauwer, L.: Tensorlab user guide (2016). http:\/\/www.tensorlab.net"}],"container-title":["Lecture Notes in Computer Science","Latent Variable Analysis and Signal Separation"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-53547-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,25]],"date-time":"2017-06-25T10:29:16Z","timestamp":1498386556000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-53547-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319535463","9783319535470"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-53547-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}