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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 15:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Learning VQ-VAE for Image Dimensionality Reduction with Spatial Frequency Loss Naoyuki Ichimura (AIST)  PRMU2023-60 |
Vector Quantized-Variational AutoEncoders (VQ-VAEs) are a type of deep neural networks designed to learn an approximate ... [more] | PRMU2023-60 pp.53-58 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 15:10 |
Okinawa | (Primary: On-site, Secondary: Online) |
Element Selection Based on Classifiability Using Nonconvex Sparse Optimization Taiga Kawamura, Natsuki Ueno, Nobutaka Ono (TMU) EA2023-83 SIP2023-130 SP2023-65 |
(To be available after the conference date) [more] | EA2023-83 SIP2023-130 SP2023-65 pp.133-138 |
| IMQ | 2023-12-22 14:00 |
Toyama | University of Toyama | [Invited Lecture]
Machine learning application for 2D/3D data analysis in material science Kentaro Kutsukake (RIKEN) IMQ2023-9 |
In materials science, data is becoming increasingly complex, high-dimension, large-scale, and numerous, consequently, hi... [more] | IMQ2023-9 pp.1-3 |
| EMM, EA, ASJ-H | 2023-11-23 13:00 |
Toyama | [Poster Presentation]
A Study of Complexity Reduction for Classification of Musical Instruments Using Element Selection Ryu Kato, Natsuki Ueno, Nobutaka Ono (Tokyo Metropolitan Univ.), Ryo Matsuda, Kazunobu Kondo (Yamaha Corp.) EA2023-37 EMM2023-68 |
In this study, we propose complexity reduction in convolutional-neural-network (CNN)-based music instruments classificat... [more] | EA2023-37 EMM2023-68 pp.51-56 |
|
| CCS, NLP | 2023-06-09 13:55 |
Tokyo | Tokyo City Univ. | Analysis of Vocal and Ventricular Folds Data Using Machine Learning Takumi Inoue, Kota Shiozawa, Isao Tokuda (Rits Univ) NLP2023-24 CCS2023-12 |
Vocal fold vibration is a nonlinear phenomenon in the real world. In humans, vocal folds can produce complex sounds by i... [more] | NLP2023-24 CCS2023-12 pp.49-52 |
| MBE, NC | 2022-12-03 11:50 |
Osaka | Osaka Electro-Communication University | Investigation of the Effect of Task Difficulty on Achievement in Motor Learning Using a Motion Imitation Learning Support System Kohei Umezawa, Takashi Isezaki, Yukio Koike, Ryosuke Aoki, Saijo Naoki, Shinji Miyahara (NTT) MBE2022-23 NC2022-45 |
(To be available after the conference date) [more] | MBE2022-23 NC2022-45 pp.11-16 |
| NLP | 2022-11-24 10:20 |
Shiga | (Primary: On-site, Secondary: Online) |
Reconstructing of Vocal Fold Vibration Video by Echo State Network and Dimensionality Reduction Tomu Noguchi, Kota Shiozawa, Isao Tokuda (Ritsumeikan Univ.) NLP2022-56 |
Video data provides an effective means for capturing the dynamics of experimental object. The dimensionality that actual... [more] | NLP2022-56 pp.1-4 |
| SIP | 2022-08-26 10:48 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Instantaneous linear dimensionality reduction for array signal processing Natsuki Ueno, Nobutaka Ono (TMU) SIP2022-65 |
Linear dimensionality reduction of time-series signals observed by a sensor array is often useful in balancing the accur... [more] | SIP2022-65 pp.81-85 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 10:55 |
Online | Online | Reward-oriented Environment Inference on Reinforcement Learning Kazuki Takahashi (Kogakuin Univ.), Tomoki Fukai (OIST), Yutaka Sakai (Tamagawa Univ.), Takashi Takekawa (Kogakuin Univ.) NC2021-42 |
Experiments on humans using the bandit problem have shown that dimensionality reduction of complex observations to a sta... [more] | NC2021-42 pp.49-54 |
| IT | 2021-07-09 14:30 |
Online | Online | Construction of Dimension Reduction Matrix for Signal Recovery of Multivariate Gaussian Vectors Kento Yokoyama, Tadashi Wadayama, Satoshi Takabe (NIT) IT2021-26 |
In compressed sensing, we discuss the problem of estimating the sparse original signal $¥bm{x} ¥in ¥mathbb{R}^n$ from th... [more] | IT2021-26 pp.63-68 |
| SIS, ITE-BCT | 2020-10-01 13:00 |
Online | Online | Evaluation of linear dimensionality reduction methods considering visual information protection for privacy-preserving machine learning Masaki Kitayama, Nobutaka Ono, Hitoshi Kiya (Tokyo Metro. Univ.) SIS2020-13 |
In this paper, linear dimensionality reduction methods are evaluated in terms of difficulty in estimating the visual inf... [more] | SIS2020-13 pp.17-22 |
| HIP, HCS, HI-SIGCOASTER [detail] | 2020-05-14 14:40 |
Online | Online | On Effective Dimensions, Riemann Metric Tensor Estimation and Dimension Reduction of Facial Expression Space Masashi Shinto, Jinhui Chao (Chuo Univ.) HCS2020-2 HIP2020-2 |
In this paper we first present definitions of effective dimensions for Riemann manifolds and psychophysical spaces. Then... [more] | HCS2020-2 HIP2020-2 pp.7-12 |
| SP, EA, SIP | 2020-03-02 10:35 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
Dimension reduction without multiplication in machine learning Nobutaka Ono (TMU) EA2019-104 SIP2019-106 SP2019-53 |
In this study, we propose a dimension reduction method for machine learning by only selecting elements without multiplic... [more] | EA2019-104 SIP2019-106 SP2019-53 pp.21-26 |
| SIS | 2019-12-12 14:35 |
Okayama | Okayama University of Science | A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning Ayana Kawamura, Kenta Iida, Hitoshi Kiya (Tokyo Metro. Univ.) SIS2019-26 |
In this paper, we propose a dimensionality reduction method with random sampling for privacy-preserving machine learning... [more] | SIS2019-26 pp.17-21 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-18 10:25 |
Okinawa | Okinawa Institute of Science and Technology | Spatial-Temporal decomposition to resting and task MEG using DMD Fumiya Nakai (NAIST), Okito Yamashita (ATR) NC2019-13 IBISML2019-11 |
Magneto/electroencephalography (M/EEG) observe neural activities with high spatial-temporal resolution without invasive ... [more] | NC2019-13 IBISML2019-11 pp.51-56(NC), pp.73-78(IBISML) |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-21 13:30 |
Fukuoka | Modification of Bayesian Optimization for Efficient Calibration of Simulation Model Daiki Kiribuchi, Takeichiro Nishikawa (Toshiba), Satoru Yokota, Ryota Narasaki, Soh Koike (Toshiba Memory) PRMU2018-63 IBISML2018-40 |
(To be available after the conference date) [more] | PRMU2018-63 IBISML2018-40 pp.195-200 |
|
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 10:25 |
Okinawa | Okinawa Institute of Science and Technology | Feature scaling in spectral classification of high dimensional data Momo Matsuda, Keiichi Morikuni, Akira Imakura, Tetsuya Sakurai (Univ. Tsukuba) IBISML2018-2 |
We consider the classification problem for high dimensional data. Using prior knowledge on the labels of partial samples... [more] | IBISML2018-2 pp.9-14 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 13:25 |
Okinawa | Okinawa Institute of Science and Technology | A supervised dimensionality reduction method using linear combinations of multiple eigenvectors Akira Imakura, Momo Matsuda, Tetsuya Sakurai (Univ. Tsukuba) IBISML2018-6 |
Dimensionality reduction methods that reduce the dimension of original data to a low-dimensional subspace such as LPP an... [more] | IBISML2018-6 pp.39-45 |
| RCS, SR, SRW (Joint) |
2018-03-01 10:00 |
Kanagawa | YRP | CSI Overhead Reduction for Massive MIMO using Multi-Dimensional Scaling Extended in Time-Domain and AR Model Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.) RCS2017-349 |
Massive MIMO (multiple-input multiple-output) is one of the technologies that has been focused in 5G (5th generation mob... [more] | RCS2017-349 pp.185-190 |
| MBE, NC (Joint) |
2017-11-24 16:50 |
Miyagi | Tohoku University | NC2017-32 |
Continuous latent variable model is a category of dimension reduction methods, which estimates low dimensional latent va... [more] | NC2017-32 pp.29-34 |
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