Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dim...
Yanwei Pang, Lei Zhang, Zhengkai Liu, Nenghai Yu, ...
This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the proposed techniques is that they attempt to preserve both...
— Orthogonal Neighborhood Preserving Projections (ONPP) is a linear dimensionality reduction technique which attempts to preserve both the intrinsic neighborhood geometry of the ...
Abstract. Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the re...
Axel J. Soto, Marc Strickert, Gustavo E. Vazquez, ...
Locality preserving projections (LPP) is a typical graph-based dimensionality reduction (DR) method, and has been successfully applied in many practical problems such as face recog...