A fast diagonal distance metric learning approach for large-scale datasets
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Publication:6092062
DOI10.1016/j.ins.2021.04.077OpenAlexW3157870166MaRDI QIDQ6092062
Tie Li, Yi Peng, Gang Kou, Philip S. Yu
Publication date: 23 November 2023
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2021.04.077
Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Smooth minimization of non-smooth functions
- Generalization bounds for metric and similarity learning
- Accounting for externalities and disposability: a directional economic environmental distance function
- A boosting approach for supervised Mahalanobis distance metric learning
- Probabilistic analysis of optimization algorithms - some aspects from a practical point of view
- Learning distance to subspace for the nearest subspace methods in high-dimensional data classification
- Deep metric learning using triplet network
- An efficient method for clustered multi-metric learning
- Supervised distance metric learning through maximization of the Jeffrey divergence
- Condition number analysis and preconditioning of the finite cell method
- Joint dimensionality reduction and metric learning for image set classification
- Thick-Restart Lanczos Method for Large Symmetric Eigenvalue Problems
- Discriminative Deep Metric Learning for Face and Kinship Verification
- Asynchronous Distributed ADMM for Large-Scale Optimization—Part I: Algorithm and<?Pub _newline ?>Convergence Analysis
- Why Are Big Data Matrices Approximately Low Rank?
- Fast Alternating Direction Optimization Methods
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