Spectral Partial Least Squares Regression

摘要：

Linear Graph Embedding (LGE) is the linearization of graph embedding, and has been applied in many domains successfully. However, the high computational cost restricts these algorithms to be applied to large scale high dimensional data sets. One major limitation of such algorithms is that the generalized eigenvalue problem is computationally expensive to solve especially for large scale problems. Spectral regression can overcome this difficulty by casting the problem of learning an embedding function into a regression framework to avoid eigen-decomposition of dense matrices. In this paper, we develop a algorithm, Spectral Partial Least Squares Regression (SPLSR), which have advantages of PLSR and spectral regression. The experimental results have demonstrated the effectiveness of our proposed algorithm.

关键词： LGE spectral regression orthonormal PLSR

作者: Jiangfeng Chen Baozong Yuan

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing, China 100044

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1351-1354

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）