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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 6 of 6  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
EA, US
(Joint)		 2023-12-22
13:00		 Fukuoka   [Poster Presentation] Multichannel Blind Source Separation Using Independent Low-Rank Matrix Analysis with Observed-Signal-Dependent Regularization Based on Spectrogram Consistency
Takaaki Kojima, Norihiro Takamune, Sota Misawa (UTokyo), Daichi Kitamura (NIT,Kagawa), Hiroshi Saruwatari (UTokyo) EA2023-51		 Independent low-rank matrix analysis (ILRMA) is the state-of-the-art technique for blind source separation under the ove... [more] EA2023-51
pp.13-20
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-03
16:25		 Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)		 Fast Identification of Possible Model Parameter Update for Low-Rank Update of Training Data
Hiroyuki Hanada, Noriaki Hashimoto (RIKEN), Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) PRMU2022-123 IBISML2022-130		 Machine learning methods often require re-training the training dataset with low-rank modifications (small number of ins... [more] PRMU2022-123 IBISML2022-130
pp.347-354
CS, CQ
(Joint)		 2021-05-14
10:25		 Online On-line [Invited Lecture] Matrix Completion Based Missing RSS Sequence Recovery and Future Value Prediction
Norisato Suga (ATR/TUS), Kazuto Yano (ATR), Julian Webber (ATR/Osaka Univ.), Yafei Hou (ATR/Okayama Univ.), Eiji Nii, Toshihide Higashimori, Yoshinori Suzuki (ATR) CQ2021-12		 This paper proposes rank minimization and matrix factorization (MF) based interpolations of received signal strength (RS... [more] CQ2021-12
pp.47-52
SIP, CAS, MSS, VLD 2017-06-20
11:40		 Niigata Niigata University, Ikarashi Campus Matrix Completion Algorithm based on Locally Linear Embedding and its Application to Signal Restoration of Nonlinear Systems
Ryohei Sasaki (TUS), Katsumi Konishi (Kogakuin Univ.), Tomohiro Takahashi, Toshihiro Furukawa (TUS) CAS2017-18 VLD2017-21 SIP2017-42 MSS2017-18		 In this paper, we deal with matrix completion problem of non-low rank matrix. In the problem of recovering the output si... [more] CAS2017-18 VLD2017-21 SIP2017-42 MSS2017-18
pp.93-97
EA, SIP, SP 2011-05-12
13:25		 Osaka Ritsumeikan Univ. Image Inpainting Based on Rank Minimization
Tomohiro Takahashi (TUS), Katsumi Konishi (Kogakuin Univ), Toshihiro Furukawa (TUS) EA2011-12 SIP2011-12 SP2011-12		 This paper proposes a rank minimization based approach to a novel image painting.
We utilize the 2-D autoregressive (AR... [more]		 EA2011-12 SIP2011-12 SP2011-12
pp.67-72
PRMU, NLC, TL 2006-10-20
14:30		 Tokyo   Simultaneous Low Rank Approximations of Tensors
Kohei Inoue, Kiichi Urahama (Kyushu Univ.) 		In this paper, we propose iterative and non-iterative algorithms for simultaneous low rank approximation of tensors (SLR... [more] PRMU2006-112
pp.47-52
 Results 1 - 6 of 6  /   
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