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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 15:00 |
Okinawa | Okinawa Institute of Science and Technology | A model selection criterion for LASSO estimate with scaling Katsuyuki Hagiwara (Mie Univ.)  IBISML2019-5 |
To relax a bias problem in LASSO (Least Absolute Shrinkage and election Operator), there have been several studies inclu... [more] | IBISML2019-5 pp.27-34 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 17:00 |
Okinawa | Okinawa Institute of Science and Technology | Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series Yoshikazu Shibahara, Takuto Sakuma (NIT), Ichiro Takeuchi (NIT/RIKEN/NIMS), Masayuki Karasuyama (NIT/NIMS) IBISML2019-9 |
In recent years, improvement of sensor performance and spread of portable devices such as smartphones enable us to easil... [more] | IBISML2019-9 pp.57-64 |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 16:25 |
Nagasaki | Fukue Culture Center | [Invited Talk]
Image Processing Based on Sparse and Low-rank Modeling Seisuke Kyochi (The Univ. of Kitakyushu) SIS2019-10 |
This paper presents fundamental tools for image recovery by convex optimization and introduces some case study from the ... [more] | SIS2019-10 pp.55-60 |
| EMM | 2019-03-14 11:30 |
Okinawa | TBD | Image Patch Modeling using Secure Computation of Sparse Dictionary Learning Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EMM2018-115 |
With the advent of the big data era, digital content continues to increase. Sparse coding is attracting attention as an... [more] |
EMM2018-115 pp.129-134 |
| SIS | 2019-03-06 15:10 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | Secure Computation of Sparse Dictionary Learning Takayuki Nakachi, Yukihiro Bandoh (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) SIS2018-43 |
With the advent of the big data era, all digital contents continue to increase. Sparse modeling is drawing attention as ... [more] | SIS2018-43 pp.35-40 |
| NC, MBE (Joint) |
2019-03-05 15:25 |
Tokyo | University of Electro Communications | Periodic Interference Cancellation Including Steep Characteristic Change With Adaptive Filter Kengo Kamei, Naohiro Toda (Aichi Pref. Univ.), Katsuyuki Hagiwara (Mie Univ.) MBE2018-100 |
When measuring bioelectric signals such as electroretinogram(ERG), periodic interference originated from power lines bec... [more] | MBE2018-100 pp.71-76 |
| MICT, MI | 2018-11-06 14:50 |
Hyogo | University of Hyogo | Feature extraction of coarse/fine crackles and its improvement via sparse modeling techniques Kosei Nishitsuji, Tomoya Sakai, Toshikazu Fukumitsu, Yasushi Obase (Nagasaki Univ.), Sueharu Miyahara (BIPS) MICT2018-48 MI2018-48 |
Medical experts have heuristically defined lung sound features and validated their relations with patients’ conditions i... [more] | MICT2018-48 MI2018-48 pp.45-48 |
| IEE-CMN, EMM, LOIS, IE, ITE-ME [detail] | 2018-09-27 13:50 |
Oita | Beppu Int'l Convention Ctr. aka B-CON Plaza | Image Patch Modeling in Encrypted Domain using Sparse Coding Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) LOIS2018-12 IE2018-32 EMM2018-51 |
Sparse coding represents observed signals effectively as a linear combination of a small number of bases which are chose... [more] | LOIS2018-12 IE2018-32 EMM2018-51 pp.13-18 |
| ICSS, IA | 2018-06-26 09:50 |
Ehime | Ehime University | Proposal of Sparse-Modeling based Approach for Betweenness Centrality Estimation Ryotaro Matsuo, Ryo Nakamura, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2018-10 ICSS2018-10 |
In recent years, a statistical approach for estimating unobserved model parameters from a small number of observations u... [more] | IA2018-10 ICSS2018-10 pp.61-66 |
| CAS, SIP, MSS, VLD | 2018-06-14 12:25 |
Hokkaido | Hokkaido Univ. (Frontier Research in Applied Sciences Build.) | [Invited Talk]
Application of sparse modeling to geosciences Tatsu Kuwatani (JAMSTEC/JST PRESTO) CAS2018-5 VLD2018-8 SIP2018-25 MSS2018-5 |
The essence of geosciences is an inverse problem. It is important to extract essential information from data with noise ... [more] | CAS2018-5 VLD2018-8 SIP2018-25 MSS2018-5 pp.23-25 |
| PRMU, MI, IE, SIP | 2018-05-17 17:00 |
Gifu | [Invited Talk]
Statistical Modeling of 3-D DFT Coefficients of Moving Images and Its Applications to Video Restoration Takahiro Saito, Takashi Komatsu (Kanagawa Univ.) SIP2018-6 IE2018-6 PRMU2018-6 MI2018-6 |
Recently we have presented moving-image denoising with 3-D Mean-Separation-type Short-Time DFT and demonstrated that it ... [more] | SIP2018-6 IE2018-6 PRMU2018-6 MI2018-6 pp.23-28 |
|
| MBE, NC (Joint) |
2018-03-14 11:15 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Evaluation of feature selection accuracy using sparse classification algorithm based on L0-norm optimization Naoki Ishibashi, Noriki Ito, Masashi Sato (UEC Tokyo), Yoshiyuki Kabashima (Tokyo Tech), Yoichi Miyawaki (UEC Tokyo/JST PRESTO) NC2017-91 |
Classification often suffers from overfitting if applied to a dataset of small sample size and high dimensionality. Dime... [more] | NC2017-91 pp.139-144 |
| IBISML | 2018-03-06 13:35 |
Fukuoka | Nishijin Plaza, Kyushu University | Applicability of Fast Decimation Algorithm
-- Sparse two-parameter Boltzmann machine as a benchmark function -- Daisuke Motoki, Shohei Watabe, Tetsuro Nikuni (Tokyo Univ. of Science) IBISML2017-103 |
A decimation algorithm was developed by Decelle et al. for an inverse problem optimization method, which sequentially re... [more] | IBISML2017-103 pp.91-95 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2017-12-22 11:20 |
Tokyo | Waseda Univ. Green Computing Systems Research Organization | A Sound Source Separation Method for Multiple Person Speech Recognition using Wavelet Analysis Based on Sound Source Position Obtained by Depth Sensor Nobuhiro Uehara, Kazuo Ikeshiro, Hiroki Imamura (Soka Univ.) SP2017-63 |
Recently, voice information guidance systems are used for only one person in operating at a city hall. To realize operat... [more] | SP2017-63 pp.79-83 |
| IA | 2017-11-15 16:05 |
Overseas | KMITL, Bangkok, Thailand | A Solution of Minimum Link Flow Problem with Sparse Modeling
-- Formulation and Preliminary Results -- Ryotaro Matsuo, Ryo Nakamura, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2017-34 |
In recent years, a statistical approach for estimating unobserved model parameters from a small number of observations u... [more] | IA2017-34 pp.23-26 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Application of Transfer Learning to Smallscale Data and Its Evaluation Using Open Datasets Arika Fukushima, Toru Yano, Shuuichiro Imahara, Hideyuki Aisu (Toshiba) IBISML2017-41 |
Large sample size of the training data is essential for high performance of prediction on machine learning. However, in... [more] |
IBISML2017-41 pp.47-53 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Compressed Sensing CT image reconstruction using Bayesian Optimization for mixing multiple image priors Tomonori Suga, Masato Inoue (Waseda Univ.) IBISML2017-73 |
In order to reduce the amount of radiation exposure, which increases the risk of cancer, many researches have been done ... [more] | IBISML2017-73 pp.283-288 |
| IA, ICSS | 2017-06-09 09:55 |
Kochi | Kochi University of Technolo, Eikokuji Campus | A Solution for Minimum Link Flow Problem with Sparse Modeling Ryotaro Matsuo, Ryo Nakamura, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2017-10 ICSS2017-10 |
In recent years, a statistical approach for estimating unobserved model parameters from a small number of measurements u... [more] | IA2017-10 ICSS2017-10 pp.53-58 |
| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Representation and Separation of Respiratory Sounds by Online Robust Principal Component Analysis Kosei Nishitsuji, Shunpei Shiwa, Tomoya Sakai (Nagasaki Univ.) PRMU2016-180 CNR2016-47 |
This paper presents an online algorithm of separating lung sounds for computer-aided diagnosis. A lung sound consists of... [more] | PRMU2016-180 CNR2016-47 pp.157-158 |
|
| IT, SIP, RCS | 2017-01-19 16:10 |
Osaka | Osaka City Univ. | Source Estimation of en-face OCT by Sparse Signal Modeling Takumi Kawamura, Shunpei Ito, Shogo Muramatsu, Samuel Choi (Niigata Univ.) IT2016-79 SIP2016-117 RCS2016-269 |
This work proposes a perform source estimation method of en-face optical coherence tomography (OCT) through sparse sign... [more] |
IT2016-79 SIP2016-117 RCS2016-269 pp.195-200 |
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