Shinichi Nakajima

Cited by

	All	Since 2015
Citations	2233	1394
h-index	17	14
i10-index	30	19

280

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2004200520062007200820092010201120122013201420152016201720182019202014 4 15 18 28 54 91 111 149 157 182 183 196 213 260 275 264

Co-authors

Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
S. Derin BabacanAmazonVerified email at amazon.com
Wojciech SamekHead of Machine Learning Group, Fraunhofer HHI, GermanyVerified email at hhi.fraunhofer.de
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Paul von Bünauidalab GmbHVerified email at idalab.de
Tsuyoshi IdeIBM T. J. Watson Research CenterVerified email at us.ibm.com
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Alexander BinderAssistant Prof., Singapore University of Technology and Design (SUTD)Verified email at sutd.edu.sg
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Ulf BrefeldLeuphana Universität LüneburgVerified email at leuphana.de
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at informatik.uni-stuttgart.de
Christoph LippertProfessor, Hasso Plattner Institute, Universität PotsdamVerified email at hpi.de
Stephan MandtAssistant Professor, University of California, IrvineVerified email at uci.edu
John CunninghamAssociate Professor, Columbia UniversityVerified email at columbia.edu
Takeuchi IchiroProfessor of Computer Science, Nagoya Institute of TechnologyVerified email at nitech.ac.jp
Kazuho WatanabeToyohashi University of TechnologyVerified email at cs.tut.ac.jp

Shinichi Nakajima

Machine Learning Group, Technische Universität Berlin

Verified email at tu-berlin.de - Homepage

Machine Learning Computer Vision


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Direct importance estimation with model selection and its application to covariate shift adaptation M Sugiyama, S Nakajima, H Kashima, P Buenau, M Kawanabe Advances in neural information processing systems 20, 1433-1440, 2007	678	2007
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1-2), 35, 2010	314	2010
Direct importance estimation for covariate shift adaptation M Sugiyama, T Suzuki, S Nakajima, H Kashima, P von Bünau, ... Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008	276	2008
Multi-class image segmentation using conditional random fields and global classification N Plath, M Toussaint, S Nakajima Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	111	2009
Bayesian group-sparse modeling and variational inference SD Babacan, S Nakajima, MN Do IEEE transactions on signal processing 62 (11), 2906-2921, 2014	100	2014
Pool-based active learning in approximate linear regression M Sugiyama, S Nakajima Machine Learning 75 (3), 249-274, 2009	71	2009
Global analytic solution of fully-observed variational Bayesian matrix factorization S Nakajima, M Sugiyama, SD Babacan, R Tomioka Journal of Machine Learning Research 14 (Jan), 1-37, 2013	69	2013
Theoretical analysis of Bayesian matrix factorization S Nakajima, M Sugiyama The Journal of Machine Learning Research 12, 2583-2648, 2011	49	2011
Perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, S Babacan Advances in Neural Information Processing Systems 25, 971-979, 2012	32	2012
Variational Bayes solution of linear neural networks and its generalization performance S Nakajima, S Watanabe Neural Computation 19 (4), 1112-1153, 2007	30	2007
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. S Nakajima, M Sugiyama, SD Babacan ICML, 497-504, 2011	26	2011
Implicit Regularization in Variational Bayesian Matrix Factorization. S Nakajima, M Sugiyama ICML, 815-822, 2010	26	2010
Multiple kernel learning for object classification S Nakajima, A Binder, C Müller, W Wojcikiewicz, M Kloft, U Brefeld, ... Proceedings of the 12th Workshop on Information-based Induction Sciences 24, 2009	24	2009
Variational Bayesian sparse additive matrix factorization S Nakajima, M Sugiyama, SD Babacan Machine learning 92 (2-3), 319-347, 2013	21	2013
An exhaustive search and stability of sparse estimation for feature selection problem K Nagata, J Kitazono, S Nakajima, S Eifuku, R Tamura, M Okada IPSJ Online Transactions 8, 25-32, 2015	20	2015
Support Vector Data Descriptions and-Means Clustering: One Class? N Görnitz, LA Lima, KR Müller, M Kloft, S Nakajima IEEE transactions on neural networks and learning systems 29 (9), 3994-4006, 2017	17	2017
Probabilistic low-rank subspace clustering S Babacan, S Nakajima, M Do Advances in Neural Information Processing Systems 25, 2744-2752, 2012	17	2012
Global analytic solution for variational Bayesian matrix factorization S Nakajima, M Sugiyama, R Tomioka Advances in Neural Information Processing Systems, 1768-1776, 2010	17	2010
Condition for perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, SD Babacan The Journal of Machine Learning Research 16 (1), 3757-3811, 2015	16	2015
Porosity estimation by semi-supervised learning with sparsely available labeled samples LA Lima, N Görnitz, LE Varella, M Vellasco, KR Müller, S Nakajima Computers & Geosciences 106, 33-48, 2017	14	2017

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