图 书 馆
使用连续一维拉普拉斯特征性映射的鲁棒非线性降维Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmapse |
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| 课程网址: | http://videolectures.net/icml07_gerber_dler/ |
| 主讲教师: | Samuel Gerber |
| 开课单位: | 犹他大学 |
| 开课时间: | 2007-07-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spectral decomposition, as used for example by the Laplacian Eigenmaps algorithm, provides a powerful tool for non-linear dimensionality reduction and manifold learning. In this paper, we discuss a significant shortcoming of these approaches, which we refer to as the repeated eigendirections problem. We propose a novel approach that combines successive 1dimensional spectral embeddings with a data advection scheme that allows us to address this problem. The proposed method does not depend on a non-linear optimization scheme; hence, it is not prone to local minima. Experiments with artificial and real data illustrate the advantages of the proposed method over existing approaches. We also demonstrate that the approach is capable of correctly learning manifolds corrupted by significant amounts of noise. |
| 关 键 词: | 噪声数据; 非线性维数; 映射算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 41 |