|
基于判别分析和K均值聚类的自适应降维方法
Adaptive Dimension Reduction Using Discriminant Analysis and K-means Clustering |
|
| |
| 课程网址: |
http://videolectures.net/icml07_ji_adr/
|
| 主讲教师: |
Shuiwang Ji
|
| 开课单位: |
华盛顿州立大学
|
| 开课时间: |
2007-07-27
|
| 课程语种: |
英语
|
| 中文简介: |
正则化的核判别分析 (rkda) 通过核技巧在特征空间中进行线性判别分析。rkda 的性能取决于内核的选择。在本文中, 我们考虑了在一组凸核上学习最优内核的问题。我们证明了在二进制案例中, 内核学习问题可以表述为半元程序 (sdp)。我们进一步将 sdp 公式扩展到多类案例。它是基于本文建立的一个关键结果, 即多类内核学习问题可以分解为一组二值级内核学习问题。此外, 我们还提出了一种近似方案, 以降低多类 sdp 公式的计算复杂度。rkda 的性能还取决于正则化参数的值。我们表明, 这个值可以在框架中自动学习。基准数据集的实验结果表明了所提出的 sdp 公式的有效性。
|
| 课程简介: |
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-class case. We further extend the SDP formulation to the multi-class case. It is based on a key result established in this paper, that is, the multi-class kernel learning problem can be decomposed into a set of binary-class kernel learning problems. In addition, we propose an approximation scheme to reduce the computational complexity of the multi-class SDP formulation. The performance of RKDA also depends on the value of the regularization parameter. We show that this value can be learned automatically in the framework. Experimental results on benchmark data sets demonstrate the efficacy of the proposed SDP formulations.
|
| 关 键 词: |
正则化核判别分析; 聚类; 预处理; 半定规划（SDP）
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-07-06：heyf
|
| 阅读次数: |
51
|
|
图 书 馆