Locality Sensitive Semi-Supervised Dimensionality Reduction on Multimodal Data
A special kind of data is considered in this paper called multimodal data. It has the property that samples in a class are from several separate clusters. Locality Preserving Projection (LPP) can work well with multimodal data due to its locality preserving property. However, the label information is not used to improve the learning performance due to the unsupervised character of LPP. In this paper, we propose a method called Locality Sensitive SemiSupervised Dimensionality Reduction (semi-LSDR). It takes both the discriminant information and geometry structure into account. Specifically, we construct a between-class graph on labeled samples and a nearest neighbor graph both from the perspective of locality. A directly mapping can be achieved by solving a generalized eigenvalue problem. Effectiveness of the proposed method is showed through simulations with benchmark data sets.
locality sensitive semi-supervised learning dimensionality reduction multimodality
Zhikai Zhao Jiansheng Qian
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, School of Information and Electrical Engineering, China University of Mining and Technology,Xuzhou 2
国际会议
大连
英文
258-261
2011-10-19（万方平台首次上网日期，不代表论文的发表时间）