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Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data
| Title: | Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data |
| Authors: | Tai, Mariko Browse this author | | Kudo, Mineichi Browse this author →KAKEN DB | | Tanaka, Akira Browse this author →KAKEN DB | | Imai, Hideyuki Browse this author →KAKEN DB | | Kimura, Keigo Browse this author |
| Keywords: | Supervised Laplacian eigenmaps | | Out-of-sample problem | | Multi-label problems | | Kernel trick | | Separability-guided feature extraction |
| Issue Date: | 26-Oct-2021 |
| Publisher: | Elsevier |
| Journal Title: | Pattern recognition |
| Volume: | 123 |
| Start Page: | 108399 |
| Publisher DOI: | 10.1016/j.patcog.2021.108399 |
| Abstract: | We had previously proposed a supervised Laplacian eigenmap for visualization (SLE-ML) that can handle multi-label data. In addition, SLE-ML can control the trade-off between the class separability and local structure by a single trade-off parameter. However, SLE-ML cannot transform new data, that is, it has the "out-of-sample" problem. In this paper, we show that this problem is solvable, that is, it is possible to simulate the same transformation perfectly using a set of linear sums of reproducing kernels (KSLEML) with a nonsingular Gram matrix. We experimentally showed that the difference between training and testing is not large; thus, a high separability of classes in a low-dimensional space is realizable with KSLE-ML by assigning an appropriate value to the trade-off parameter. This offers the possibility of separability-guided feature extraction for classification. In addition, to optimize the performance of KSLEML, we conducted both kernel selection and parameter selection. As a result, it is shown that parameter selection is more important than kernel selection. We experimentally demonstrated the advantage of using KSLE-ML for visualization and for feature extraction compared with a few typical algorithms. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) |
| Type: | article |
| URI: | http://hdl.handle.net/2115/83634 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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