手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| Locally Linear Embedding (LLE)（局所線形埋め込み）× | サポートベクターマシン（分類）× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2000 | 1995 |
| 提唱者≠ | Sam Roweis & Lawrence Saul | Cortes, C. & Vapnik, V. |
| 種類≠ | Nonlinear manifold dimensionality reduction | Maximum-margin classifier (kernel method) |
| 原典≠ | Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| 別名 | LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| 関連≠ | 3 | 5 |
| 概要≠ | Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateデータセット ↗ |
|
|