| 大会名称 |
|---|
| 2018年 情報科学技術フォーラム(FIT) |
| 大会コ－ド |
| F |
| 開催年 |
| 2018 |
| 発行日 |
| 2018-09-12 |
| セッション番号 |
| 6f |
| セッション名 |
| 機械学習（４） |
| 講演日 |
| 2018/09/21 |
| 講演場所(会議室等) |
| D棟D23 |
| 講演番号 |
| ID-002 |
| タイトル |
| Scaling Locally Linear Embedding |
| 著者名 |
| Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda, |
| キーワード |
| LLE, Efficient, Dimensionality Reduction |
| 抄録 |
|
Locally Linear Embedding (LLE) is a popular approach to dimensionality reduction. For dimensionality reduction, it computes a nearest neighbor graph from a given dataset where edge weights are obtained by applying the Lagrange multiplier method, and it then computes eigenvectors of the LLE kernel where the edge weights are used to obtain the kernel. Although LLE is used in many applications, its computation cost is significantly high. Our approach, Ripple, is based on two ideas: (1) it incrementally updates the edge weights by exploiting the Woodbury formula and (2) it efficiently computes eigenvectors of the LLE kernel by exploiting the LU decomposition-based inverse power method. Experiments show that Ripple is significantly faster than the original approach of LLE by guaranteeing the same results of dimensionality reduction. |
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