Semisupervised Tangent Space Discriminant Analysis

Abstract EN

A novel semisupervised dimensionality reduction method named Semisupervised Tangent Space Discriminant Analysis (STSD) is presented, where we assume that data can be well characterized by a linear function on the underlying manifold.

For this purpose, a new regularizer using tangent spaces is developed, which not only can capture the local manifold structure from both labeled and unlabeled data, but also has the complementarity with the Laplacian regularizer.

Furthermore, STSD has an analytic form of the global optimal solution which can be computed by solving a generalized eigenvalue problem.

To perform nonlinear dimensionality reduction and process structured data, a kernel extension of our method is also presented.

Experimental results on multiple real-world data sets demonstrate the effectiveness of the proposed method.