IEICE Technical Committee Submission System
Conference Schedule
Online Proceedings
[Sign in]
Tech. Rep. Archives
    [Japanese] / [English] 
( Committee/Place/Topics  ) --Press->
 
( Paper Keywords:  /  Column:Title Auth. Affi. Abst. Keyword ) --Press->

All Technical Committee Conferences  (Searched in: All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 47  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP, MSS 2023-03-17
14:30
Nagasaki
(Primary: On-site, Secondary: Online)
Global Convergence Analysis of Distributed HALS Algorithm for Nonnegative Matrix Factorization
Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-104 NLP2022-149
As a fast computational method for Nonnegative Matrix Factorization (NMF),
the Hierarchical Alternating Least Squares ... [more]
MSS2022-104 NLP2022-149
pp.198-203
NLP, MSS 2023-03-17
14:50
Nagasaki
(Primary: On-site, Secondary: Online)
Reformulation of Optimization Problem in Randomized NMF and Proposal of A Novel Iterative Update Algorithm
Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-105 NLP2022-150
As an approach to efficiently perform large-scale Nonnegative Matrix Factorization (NMF), a randomized NMF was recently ... [more] MSS2022-105 NLP2022-150
pp.204-209
EA, US
(Joint)
2022-12-23
09:00
Hiroshima Satellite Campus Hiroshima Proposal of Speech Decomposition Algorithm by Cepstral-Basis-Decomposed Nonnegative Matrix Factorization and Application to Speech Source Separation Technique
Fuga Oshima, Masashi Nakayama (Hiroshima City) EA2022-69
Nonnegative matrix factorization (NMF) is the algorithm that effectively represents acoustical signals by inputting ampl... [more] EA2022-69
pp.49-54
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-17
15:00
Online Online Blind Source Separation based on Independent Low-Rank Matrix Analysis using Restricted Boltzmann Machines
Shotaro Furuta, Takuya Kishida, Toru Nakashika (UEC) SP2022-8
In this paper, we propose a new blind source separation method that combines independent low-rank source separation (ILR... [more] SP2022-8
pp.26-29
SIP 2021-08-24
10:00
Online Online [Invited Talk] Audio source separation based on independent low-rank matrix analysis and its extensions
Daichi Kitamura (NIT Kagawa) SIP2021-32
Audio source separation is a technique for separating individual audio sources from an observed mixture signal. In parti... [more] SIP2021-32
pp.19-24
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2021-06-28
13:50
Online Online Simplification of Average Consensus Algorithm in Distributed HALS Algorithm for NMF
Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-3 IBISML2021-3
Nonnegative Matrix Factorization (NMF) is the process of approximating a given nonnegative matrix by the product of two ... [more] NC2021-3 IBISML2021-3
pp.15-22
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2021-06-28
14:15
Online Online Modification of Optimization Problem in Randomized NMF and Design of Optimization Method based on HALS Algorithm
Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-4 IBISML2021-4
Nonnegative matrix factorization (NMF) is the process of decomposing a given nonnegative matrix into two nonnegative fac... [more] NC2021-4 IBISML2021-4
pp.23-30
SIS, IPSJ-AVM 2021-06-24
14:15
Online Online [Tutorial Lecture] Noise Reduction using Nonnegative Matrix Factorization
Motoaki Mour (Aichi Univ.) SIS2021-9
Nonnegative matrix factorization (NMF) is a general term for methods that factorize a matrix into two or more matrices w... [more] SIS2021-9
pp.49-54
EA, US, SP, SIP, IPSJ-SLP [detail] 2021-03-04
10:15
Online Online A quantitative measure of discriminability between NMF dictionaries
Eisuke Konno, Daisuke Saito, Nobuaki Minematsu (UTokyo) EA2020-82 SIP2020-113 SP2020-47
Supervised nonnegative matrix factorization (NMF) is a popular approach for monaural audio source separation. It realize... [more] EA2020-82 SIP2020-113 SP2020-47
pp.134-139
MSS, CAS, IPSJ-AL [detail] 2020-11-25
17:00
Online Online Distributed Algorithms based on Multiplicative Update Rules for Nonnegative Matrix Factorization
Yohei Domen, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) CAS2020-24 MSS2020-16
Nonnegative matrix factorization (NMF) is a multivariate method that approximates a given nonnegative matrix by the prod... [more] CAS2020-24 MSS2020-16
pp.28-33
NC, MBE
(Joint)
2020-03-05
14:40
Tokyo University of Electro Communications
(Cancelled but technical report was issued)
A method for sound source separation using the onset information based on the NMF with deformable bases
Shota Uchida, Susumu Kuroyanagi (NITech) NC2019-98
Currently, the NMF with deformable bases has been proposed as a model for learning the sequences of frequency spectrum f... [more] NC2019-98
pp.131-136
SIS 2019-12-12
15:45
Okayama Okayama University of Science [Invited Talk] Consensus-Based Distributed Algorithms and Applications to Machine Learning
Norikazu Takahashi (Okayama Univ.) SIS2019-29
Recently, consensus-based distributed optimization methods for multi-agent systems have been vigorously studied in the f... [more] SIS2019-29
p.35
IE, EMM, LOIS, IEE-CMN, ITE-ME, IPSJ-AVM [detail] 2019-09-20
15:25
Niigata Tokimeito, Niigata University Analysis of Daily Activities and Intervention Acceptability Using Nonnegative Tensor Factorization
Masahiro Kohjima, Masami Takahashi, Takeshi Kurashima, Tatsushi Matsubayashi, Hiroyuki Toda (NTT) LOIS2019-18 IE2019-31 EMM2019-75
In order to improve people's lifestyles to prevent lifestyle-related diseases, it is important to understand not only th... [more] LOIS2019-18 IE2019-31 EMM2019-75
pp.97-102
EA, SIP, SP 2019-03-14
13:30
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] An Initialization Method for Multichannel Nonnegative Matrix Factorization using Nonnegative Independent Component Analysis
Takahiro Ushijima, Takanobu Uramoto, Shingo Uenohara, Ken'ichi Furuya (Oita Univ.) EA2018-105 SIP2018-111 SP2018-67
Recently, devices that handle voice have become widely used, and there is a demand for a technique to extract only the t... [more] EA2018-105 SIP2018-111 SP2018-67
pp.37-42
OFT, OCS, OPE
(Joint) [detail]
2019-02-15
14:25
Fukuoka   Maximum and Minimum strain extraction from BGS observations including noise and optical loss using nonnegative matrix factorization
Takuya Fujimoto, Hiroshi Naruse (Mie Univ.), Takanori Nishino (Meijo Univ.) OFT2018-81 OPE2018-210
We have proposed a method for extracting the maximum and minimum strains produced in a Brillouin gain spectrum (BGS) obs... [more] OFT2018-81 OPE2018-210
pp.57-62(OFT), pp.89-94(OPE)
NLP, NC
(Joint)
2019-01-24
15:20
Hokkaido The Centennial Hall, Hokkaido Univ. A New Method for Deriving Multiplicative Update Rules for NMF with Error Functions Containing Logarithm
Akihiro Koso, Norikazu Takahashi (Okayama Univ.) NLP2018-122
Nonnegative Matrix Factorization (NMF) is an operation that decomposes a given nonnegative matrix X into two nonnegative... [more] NLP2018-122
pp.137-142
OFT 2018-10-11
16:00
Miyagi Touhoku Univ. [Poster Presentation] Maximum and Minimum strain extraction from Brillouin gain spectrum using nonnegative matrix factorization
Takuya Fujimoto, Hiroshi Naruse (Mie Univ.), Takanori Nishino (Meijo Univ.) OFT2018-48
A fiber optic strain measurement based on the Brillouin scattering phenomenon enables a distributed strain measurement a... [more] OFT2018-48
pp.157-161
NLP 2018-08-09
09:30
Kagawa Saiwai-cho Campus, Kagawa Univ. Derivation and Experimental Evaluation of a Novel Nonnegative Matrix Factorization Algorithm for Discovering Communities
Yoshito Usuzaka, Norikazu Takahashi (Okayama Univ.) NLP2018-64
Community discovery is an important technique for a better understanding of the structure of a network. We consider the ... [more] NLP2018-64
pp.57-62
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
13:00
Okinawa   [Poster Presentation] Performance Evaluation of Initial Value Setting Method for Spatial Correlation Matrices in Multi-channel NMF
Yu Tajima, Akira Tanaka (Hokkaido Univ.) EA2017-130 SIP2017-139 SP2017-113
The multi-channel nonnegative matrix factorization (MNMF) is an extension of the single-channel nonnegative matrix facto... [more] EA2017-130 SIP2017-139 SP2017-113
pp.161-162
MBE, NC
(Joint)
2018-03-13
10:00
Tokyo Kikai-Shinko-Kaikan Bldg.
Yuma Saito, Tsubasa Ito (Tokyo Tech), Keisuke Ota, Masanori Murayama (RIKEN), Toru Aonishi (Tokyo Tech) NC2017-68
Recent rapid progress of imaging techniques such as two-photon microscopes causes the extreme increase in amount of acqu... [more] NC2017-68
pp.3-8
 Results 1 - 20 of 47  /  [Next]  
Choose a download format for default settings. [NEW !!]
Text format pLaTeX format CSV format BibTeX format
Copyright and reproduction : All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan