DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections

High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dimensional data in an optimal lowdimensional subspace, which is learned with a few user supplied constraints as well as the structure of input data. We study two types of constraints that indicate whether or not pairs of data points originate from the same class. Data partitions that satisfy both types of constraints may be conflicting. To solve this problem, our method projects data into two different subspaces, one in the kernel space and one in the original input space, each is designed for enforcing one type of constraints. Projections in the two spaces interact and data are embedded in an optimal lowdimensional subspace where constraints are maximally satisfied. Besides constraints, our method also preserves the intrinsic data structure, such that nearby/far away data points in the original space are still...

• Type: Journal
• Year: 2010
• Where: WEBI
• Authors: Su Yan, Sofien Bouaziz, Dongwon Lee
• Keywords: Data Points, Dimensionality Reduction, Internet Technology, Optimal Lowdimensional Subspace, WEBI 2010