0 citations0 references

Manifold Discriminant Analysis

2009 IEEE Conference on Computer Vision and Pattern Recognition2009pp. 429–436

Citations Over TimeTop 10% of 2009 papers

Abstract

This paper presents a novel discriminative learning method, called manifold discriminant analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we formulate the problem as classification-oriented multi-manifolds learning. Aiming at maximizing “manifold margin”, MDA seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced. As a result, new testing manifold can be more reliably classified in the learned embedding space. The proposed method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization. Comprehensive comparisons and extensive experiments demonstrate the effectiveness of our method.

Related Papers

Manifold learning and manifold alignment based on coupled linear projections(2010)
→ Multiple Manifold Learning by Nonlinear Dimensionality Reduction(2011)10 cited
A Comparative Study of Some Manifold Learning Algorithms(2009)
→ Sparse Manifold Learning based on Laplacian Matrix(2019)
→ Semi-Supervised Manifold Learning for Hyperspectral Data(2019)