Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural gradient works efficiently in learning
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
A Bayesian approach to geometric subspace estimation
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Sparse Representation for Coarse and Fine Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning independent components on the orthogonal group of matrices by retractions
Neural Processing Letters
From Scores to Face Templates: A Model-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-stage optimal component analysis
Computer Vision and Image Understanding
Stochastic orthogonal and nonorthogonal subspace basis pursuit
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Image retrieval based on intrinsic spectral histogram representation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimal dimension reduction for image retrieval with correlation metrics
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Face recognition using optimal linear components of range images
Image and Vision Computing
Accurate estimation of ICA weight matrix by implicit constraint imposition using lie group
IEEE Transactions on Neural Networks
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
Tools for application-driven linear dimension reduction
Neurocomputing
Learning optimal representations for image retrieval applications
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Recognition of digital images of the human face at ultra low resolution via illumination spaces
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Kernel methods for nonlinear discriminative data analysis
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A note on sliced inverse regression with missing predictors
Statistical Analysis and Data Mining
Image and Vision Computing
Fitting smoothing splines to time-indexed, noisy points on nonlinear manifolds
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
Dimension reduction for the conditional kth moment via central solution space
Journal of Multivariate Analysis
Unscented Kalman Filtering on Riemannian Manifolds
Journal of Mathematical Imaging and Vision
Structure preserving non-negative matrix factorization for dimensionality reduction
Computer Vision and Image Understanding
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Abstract--Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.