| Presentation | 2020-06-29 Performance comparison of autoencoders and sparse PCAs Masumi Ishikawa, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Principal component analysis (PCA) is an effective tool for clarifying data structure. Each principal component includes almost all variables, which hinders understanding of features of principal components. To decrease the number of variables in principal components, extensive research on sparse PCA using L1-norm has been carried out. Since optimization with L1-norm is reduced to quadratic programming, the reduction of computational cost is a major concern. In the field of neural networks, autoencoders are used for information compression. Sparse autoencoders are also studied with L1-norm and other regularization terms. The author proposes to introduce the concept of PCA into autoencoders. In other words, the paper pursues autoencoders with larger explanation capability (i.e., larger cumulated contribution rate) and fewer variables in principal components. The paper successfully develops superior autoencoders compared with sparse PCA based on L1-norm. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Autoencoder / Sparse PCA / Sparse autoencoder / L1-norm / contribution rate |
| Paper # | NC2020-4,IBISML2020-4 |
| Date of Issue | 2020-06-22 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
|---|---|
| Conference Date | 2020/6/29(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Kazuyuki Samejima(Tamagawa Univ) / Ichiro Takeuchi(Nagoya Inst. of Tech.) / Hiroyuki Kurata(Kyutech) / Masakazu Sekijima(Tokyo Tech) |
| Vice Chair | Rieko Osu(Waseda Univ.) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Rieko Osu(NTT) / Masashi Sugiyama(ATR) / Koji Tsuda(AIST) / (NTT) / (Chuo Univ.) |
| Assistant | Ken Takiyama(TUAT) / Nobuhiko Wagatsuma(Toho Univ.) / Atsuyoshi Nakamura(Hokkaido Univ.) / Shigeyuki Oba(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / IPSJ Special Interest Group on Bioinformatics and Genomics / IPSJ Special Interest Group on Mathematical Modeling and Problem Solving |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance comparison of autoencoders and sparse PCAs |
| Sub Title (in English) | |
| Keyword(1) | Autoencoder |
| Keyword(2) | Sparse PCA |
| Keyword(3) | Sparse autoencoder |
| Keyword(4) | L1-norm |
| Keyword(5) | contribution rate |
| 1st Author's Name | Masumi Ishikawa |
| 1st Author's Affiliation | Kyushu Institute of Technology(Kyutech) |
| Date | 2020-06-29 |
| Paper # | NC2020-4,IBISML2020-4 |
| Volume (vol) | vol.120 |
| Number (no) | NC-79,IBISML-80 |
| Page | pp.pp.21-26(NC), pp.21-26(IBISML), |
| #Pages | 6 |
| Date of Issue | 2020-06-22 (NC, IBISML) |