图 书 馆
一类维数约简技术的可扩展两阶段方法A Scalable Two-Stage Approach for a Class of Dimensionality Reduction Techniques |
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| 课程网址: | http://videolectures.net/kdd2010_sun_stsa/ |
| 主讲教师: | Liang Sun |
| 开课单位: | 亚利桑那州立大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation, which can then be solved efficiently. However, the equivalence relationship only holds under certain assumptions without regularization, which severely limits their applicability in practice. In this paper, an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques, including Canonical Correlation Analysis, Orthonormal Partial Least Squares, linear Discriminant Analysis, and Hypergraph Spectral Learning. The proposed two-stage approach scales linearly in terms of both the sample size and data dimensionality. The main contributions of this paper include (1) we rigorously establish the equivalence relationship between the proposed two-stage approach and the original formulation without any assumption; and (2) we show that the equivalence relationship still holds in the regularization setting. We have conducted extensive experiments using both synthetic and real-world data sets. Our experimental results confirm the equivalence relationship established in this paper. Results also demonstrate the scalability of the proposed two-stage approach. |
| 关 键 词: | 高维数据; 特征值问题; 等价关系 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：cwx |
| 阅读次数: | 33 |