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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI, MICT | 2016-09-16 14:25 |
Tokyo | Koganei Campus, Tokyo University of Agriculture and Technology | [Invited Talk]
Tensor Completion based on Low-Rank and Smooth Structures Tatsuya Yokota (NITECH)  MICT2016-42 MI2016-56 |
Completion is a procedure that facilitates the estimation of the values of missing elements of array data, using only th... [more] | MICT2016-42 MI2016-56 pp.35-40 |
| MI | 2015-03-02 09:17 |
Okinawa | Hotel Miyahira | 4D-MRI Reconstruction using the low-rank plus sparse matrix decomposition Yukinojo Kitakami, Takashi Ohnishi, Yoshitada Masuda (Chiba Univ. Engineering), Koji Matsumoto (Chiba University Hospital), Hideaki Haneishi (Chiba Univ. Engineering) MI2014-54 |
4D-MRI can visualize and quantify the three-dimensional dynamics of the thoracoabdominal respiratory movement and allows... [more] | MI2014-54 pp.7-11 |
| PRMU, CNR | 2014-02-14 10:40 |
Fukuoka | [Special Talk]
Convex Optimization for Applications to Image Analysis and Processing
-- With a focus on effective utilization of the nuclear norm -- Shunsuke Ono, Masao Yamagishi, Isao Yamada (Tokyo Inst. of Tech.) PRMU2013-158 CNR2013-66 |
Remarkable advances in convex optimization have enabled us to handle large-scale (constrained) convex optimization probl... [more] | PRMU2013-158 CNR2013-66 p.147 |
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| MI | 2014-01-27 09:25 |
Okinawa | Bunka Tenbusu Kan | Preliminary study on fast 4D-MRI acquisition by using sparse and low-rank structures Yukinojo Kitakami, Takashi Ohnishi (Chiba Univ), Yoshitada Masuda, Koji Matsumoto (Chiba University Hospital), Hideaki Haneishi (Chiba Univ) MI2013-91 |
4D-MRI can visualize and quantify the three-dimensional dynamics of the thoracoabdominal respiratory movement and allows... [more] | MI2013-91 pp.193-198 |
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Global Solvers for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-37 |
Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning, is generally per... [more] |
IBISML2013-37 pp.7-14 |
| SP, EA, SIP | 2013-05-17 16:50 |
Okayama | Blockwise Low-Rank Prior for Cartoon-Texture Image Decomposition Shunsuke Ono, Isao Yamada (Tokyo Inst. of Tech.) EA2013-28 SIP2013-28 SP2013-28 |
We develop a cartoon-texture decomposition model with a novel texture characterization for image analysis and restoratio... [more] | EA2013-28 SIP2013-28 SP2013-28 pp.163-168 |
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| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Differential Privacy of Positive Semi-definite Matrices Jun Sakuma (U. Tsukuba) IBISML2012-46 |
, , , , [more] | IBISML2012-46 pp.89-96 |
| IN, RCS (Joint) |
2012-05-17 15:15 |
Tokyo | Kuramae-Kaikan, Tokyo Institute of Technology | A Missing Data Recovery Scheme Using Low-Rank Approximation in Wireless Sensor Networks Takahiro Matsuda (Osaka Univ.) IN2012-15 |
In wireless network, error-prone wireless links may cause loss of transmitted data. In this article, we propose a missin... [more] | IN2012-15 pp.19-24 |
| SIP, RCS | 2011-01-20 15:20 |
Kagoshima | Generalizing the multiple measurement setting from sparse vector recovery to low-rank matrix recovery Silvia Gandy, Isao Yamada (Tokyo Inst. of Tech.) SIP2010-92 RCS2010-222 |
We discuss a novel multiple measurement setting for low-rank matrix recovery in analogy to the approach taken in the spa... [more] | SIP2010-92 RCS2010-222 pp.137-142 |
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| IE, SIP | 2005-04-22 13:25 |
Tokyo | Minimum-Variance Pseudo-Unbiased Low-Rank Estimation
-- A Generalization of Marquardt's Estimator for Ill-Conditioned Inverse Problems -- Jamal Elbadraoui, Isao Yamada (Tokyo Inst. of Tech.) |
This paper presents a novel low-rank linear statistical estimator named minimum-variance pseudo-unbiased low-rank estima... [more] | SIP2005-6 IE2005-6 pp.31-36 |
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