Canonical Stiefel quotient and its application to generic face recognition in illumination spaces
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Supervised subspace learning with multi-class lagrangian SVM on the grassmann manifold
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
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Inspired by the underlying relationship between classification capability and the mutual information, in this paper, we first establish a quantitative model to describe the information transmission process from feature extraction to final classification and identify the critical channel in this propagation path, and then propose a Maximum Effective Information Criteria for pursuing the optimal subspace in the sense of preserving maximum information that can be conveyed to final decision. Considering the orthogonality and rotation invariance properties of the solution space, we present a Conjugate Gradient method constrained on a Grassmann manifold to exploit the geometric traits of the solution space for enhancing the efficiency of optimization. Comprehensive experiments demonstrate that the framework integrating the Maximum Effective Information Criteria and Grassmann manifold-based optimization method significantly improves the classification performance.