The algebraic eigenvalue problem
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Neurocomputing: foundations of research
The nature of statistical learning theory
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Pivoted document length normalization
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Parallel Optimization: Theory, Algorithms and Applications
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The Perceptron Algorithm with Uneven Margins
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Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
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Metric learning by discriminant neighborhood embedding
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Face recognition using classification-based linear projections
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Metric embedding for kernel classification rules
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Classification with partial labels
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Metric Learning: A Support Vector Approach
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Neural Computation
Distance metric learning by minimal distance maximization
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Nearest neighbour distance matrix classification
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Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
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Fast neighborhood component analysis
Neurocomputing
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Distance metric learning with eigenvalue optimization
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Online learning in the embedded manifold of low-rank matrices
The Journal of Machine Learning Research
Metric and kernel learning using a linear transformation
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Similarity learning in nearest neighbor and application to information retrieval
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
Random forests for metric learning with implicit pairwise position dependence
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Nearest neighbor classifier generalization through spatially constrained filters
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Mirror descent for metric learning: a unified approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseudo-metric. The pseudo-metrics we use are quadratic forms parameterized by positive semi-definite matrices. The core of the algorithm is an update rule that is based on successive projections onto the positive semi-definite cone and onto half-space constraints imposed by the examples. We describe an efficient procedure for performing these projections, derive a worst case mistake bound on the similarity predictions, and discuss a dual version of the algorithm in which it is simple to incorporate kernel operators. The online algorithm also serves as a building block for deriving a large-margin batch algorithm. We demonstrate the merits of the proposed approach by conducting experiments on MNIST dataset and on document filtering.