Fetching the paper…
Reading the bibliography…
Distance metric learning is a branch of machine learning that aims to learn distances from the data, which enhances the performance of similarity-based algorithms.
The use of multiple measurements in taxonomic problems,
R. A. Fisher, · 1936
Earlier work this paper cites.
G. S. Sebestyen, Decision-making processes in pattern recognition, Macmillan, 1962
1962
Earlier work this paper cites.
N. J. Nilsson, Learning machines: foundations of trainable pattern-classifying systems, McGraw-Hill, 1965
1965
Earlier work this paper cites.
Nearest neighbor pattern classification,
T. Cover, P. Hart, · 1967
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations,
J. MacQueen, et al., · 1967
Earlier work this paper cites.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming,
L. M. Bregman, · 1967
Earlier work this paper cites.
A dendrite method for cluster analysis,
T. Caliński, J. Harabasz, · 1974
Earlier work this paper cites.
W. Rudin, Real and complex analysis, Tata McGraw-Hill Education, 1987
1987
Earlier work this paper cites.
On minimizing the maximum eigenvalue of a symmetric matrix,
M. L. Overton, · 1988
Earlier work this paper cites.
Computing a nearest symmetric positive semidefinite matrix,
N. J. Higham, · 1988
Earlier work this paper cites.
R. A. Horn, C. R. Johnson, Matrix analysis, Cambridge university press, 1990
1990
Earlier work this paper cites.
An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms,
D. Wettschereck, T. G. Dietterich, · 1995
Earlier work this paper cites.
No free lunch theorems for optimization,
D. H. Wolpert, W. G. Macready, · 1997
Earlier work this paper cites.
A bootstrap technique for nearest neighbor classifier design,
Y. Hamamoto, S. Uchimura, S. Tomita, · 1997
Earlier work this paper cites.
Refining initial points for k-means clustering.,
P. S. Bradley, U. M. Fayyad, · 1998
Earlier work this paper cites.
A tutorial on support vector machines for pattern recognition,
C. J. Burges, · 1998
Earlier work this paper cites.
Nonlinear component analysis as a kernel eigenvalue problem,
B. Schölkopf, A. Smola, K.-R. Müller, · 1998
Earlier work this paper cites.
Fisher discriminant analysis with kernels,
S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K.-R. Mullers, · 1999
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, Learning from labeled and unlabeled data with label propagation, Technical Report, Carnegie Mellon University, 2002
2002
Earlier work this paper cites.
I. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, 2002
2002
Earlier work this paper cites.
Distance metric learning with application to clustering with side-information,
E. P. Xing, M. I. Jordan, S. J. Russell, A. Y. Ng, · 2003
Earlier work this paper cites.
S. Boyd, J. Dattorro, Alternating projections, Technical Report, Stanford University, 2003. Lecture notes of EE392o, Autumn Quarter
2003
Earlier work this paper cites.
S. Boyd, L. Vandenberghe, Convex optimization, Cambridge university press, 2004
2004
Earlier work this paper cites.
Neighbourhood components analysis,
J. Goldberger, G. E. Hinton, S. T. Roweis, R. R. Salakhutdinov, · 2005
Earlier work this paper cites.
Penalty function methods for constrained optimization with genetic algorithms,
Ö. Yeniay, · 2005
Earlier work this paper cites.
D. Cai, X. He, J. Han, D. Cai, X. He, J. Han, Subspace learning based on tensor analysis, Technical Report, 2005
2005
Earlier work this paper cites.
Survey of clustering algorithms,
R. Xu, D. Wunsch, · 2005
Earlier work this paper cites.
Distance metric learning: A comprehensive survey,
L. Yang, R. Jin, · 2006
Earlier work this paper cites.
T. M. Cover, J. A. Thomas, Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing), Wiley-Interscience, 2006
2006
Earlier work this paper cites.
Metric learning by collapsing classes,
A. Globerson, S. T. Roweis, · 2006
Earlier work this paper cites.
Random forests and adaptive nearest neighbors,
Y. Lin, Y. Jeon, · 2006
Earlier work this paper cites.
Svm-knn: Discriminative nearest neighbor classification for visual category recognition,
H. Zhang, A. C. Berg, M. Maire, J. Malik, · 2006
Earlier work this paper cites.
Feature extraction by maximizing the average neighborhood margin,
F. Wang, C. Zhang, · 2007
Earlier work this paper cites.
Information-theoretic metric learning,
J. V. Davis, B. Kulis, P. Jain, S. Sra, I. S. Dhillon, · 2007
Earlier work this paper cites.
Large margin component analysis,
L. Torresani, K.-c. Lee, · 2007
Earlier work this paper cites.
A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set,
A. Ahmad, L. Dey, · 2007
Earlier work this paper cites.
B. Dacorogna, Direct methods in the calculus of variations, volume 78, Springer Science & Business Media, 2007
2007
Earlier work this paper cites.
Differential entropic clustering of multivariate gaussians,
J. V. Davis, I. S. Dhillon, · 2007
Earlier work this paper cites.
J. A. Lee, M. Verleysen, Nonlinear dimensionality reduction, Springer Science & Business Media, 2007
2007
Earlier work this paper cites.
Kernel methods in machine learning,
T. Hofmann, B. Schölkopf, A. J. Smola, · 2008
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification,
K. Q. Weinberger, L. K. Saul, · 2009
Cited alongside, same era.
Is that you? metric learning approaches for face identification,
M. Guillaumin, J. Verbeek, C. Schmid, · 2009
Cited alongside, same era.
Exact bootstrap k-nearest neighbor learners,
B. M. Steele, · 2009
Cited alongside, same era.
Inference-driven metric learning for graph construction,
P. S. Dhillon, P. P. Talukdar, K. Crammer, · 2010
Cited alongside, same era.
A new kernelization framework for mahalanobis distance learning algorithms,
R. Chatpatanasiri, T. Korsrilabutr, P. Tangchanachaianan, B. Kijsirikul, · 2010
Cited alongside, same era.
Trace optimization and eigenproblems in dimension reduction methods,
E. Kokiopoulou, J. Chen, Y. Saad, · 2011
Cited alongside, same era.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning,
N. Papernot, P. McDaniel, · 2018
Closest in time.
A tutorial on bayesian optimization,
P. I. Frazier, · 2018
Closest in time.
Reinforcement learning for evolutionary distance metric learning systems improvement,
B. Ali, W. Kalintha, K. Moriyama, M. Numao, K.-i. Fukui, · 2018
Closest in time.
Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning,
Z. Ma, S. Zhou, X. Wu, H. Zhang, W. Yan, S. Sun, J. Zhou, · 2019
Closest in time.
Multi-modal media retrieval via distance metric learning for potential customer discovery,
Y. Liu, Z. Gu, T. H. Ko, J. Liu, · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hamming distance metric learning,
M. Norouzi, D. J. Fleet, R. R. Salakhutdinov, · 2012
Cited alongside, same era.
On text clustering with side information,
C. C. Aggarwal, Y. Zhao, S. Y. Philip, · 2012
Cited alongside, same era.
Distance metric learning with eigenvalue optimization,
Y. Ying, P. Li, · 2012
Cited alongside, same era.
Eigenjoints-based action recognition using naive-bayes-nearest-neighbor,
X. Yang, Y. L. Tian, · 2012
Cited alongside, same era.
Non-linear metric learning,
D. Kedem, S. Tyree, F. Sha, G. R. Lanckriet, K. Q. Weinberger, · 2012
Cited alongside, same era.
Metric learning: A survey,
B. Kulis, et al., · 2013
Cited alongside, same era.
Visual content-based web page categorization with deep transfer learning and metric learning,
D. Lopez-Sanchez, A. G. Arrieta, J. M. Corchado, · 2019
Closest in time.
Semi-supervised metric learning-based anchor graph hashing for large-scale image retrieval,
H. Hu, K. Wang, C. Lv, J. Wu, Z. Yang, · 2019
Closest in time.
Kernel distance metric learning using pairwise constraints for person re-identification,
B. Nguyen, B. De Baets, · 2019
Closest in time.
Weighted graph embedding-based metric learning for kinship verification,
J. Liang, Q. Hu, C. Dang, W. Zuo, · 2019
Closest in time.
A hybrid approach with optimization-based and metric-based meta-learner for few-shot learning,
D. Wang, Y. Cheng, M. Yu, X. Guo, T. Zhang, · 2019
Closest in time.
Detection of gh pituitary tumors based on mnf,
Y. Du, C. Liu, B. Zhang, · 2019
Closest in time.
Escaping the curse of dimensionality in similarity learning: Efficient frank-wolfe algorithm and generalization bounds,
K. Liu, A. Bellet, · 2019
Closest in time.
Tunability: Importance of hyperparameters of machine learning algorithms.,
P. Probst, A.-L. Boulesteix, B. Bischl, · 2019
Closest in time.
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations,
D. Charte, F. Charte, S. García, F. Herrera, · 2019
Closest in time.
Metric learning for multi-output tasks,
W. Liu, D. Xu, I. Tsang, W. Zhang, · 2019
Closest in time.
Transferring knowledge fragments for learning distance metric from a heterogeneous domain,
Y. Luo, Y. Wen, T. Liu, D. Tao, · 2019
Closest in time.
Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation,
A. Gómez-Ríos, S. Tabik, J. Luengo, A. Shihavuddin, B. Krawczyk, F. Herrera, · 2019
Closest in time.
Directional statistics-based deep metric learning for image classification and retrieval,
X. Zhe, S. Chen, H. Yan, · 2019
Closest in time.
Deep metric learning to rank,
F. Cakir, K. He, X. Xia, B. Kulis, S. Sclaroff, · 2019
Closest in time.
Hyperspectral imagery classification with deep metric learning,
X. Cao, Y. Ge, R. Li, J. Zhao, L. Jiao, · 2019
Closest in time.
A survey of optimization methods from a machine learning perspective,
S. Sun, Z. Cao, H. Zhu, J. Zhao, · 2019
Closest in time.
A multi-feature image retrieval scheme for pulmonary nodule diagnosis,
G. Wei, M. Qiu, K. Zhang, M. Li, D. Wei, Y. Li, P. Liu, H. Cao, M. Xing, F. Yang, · 2020
Closest in time.
Improving malicious urls detection via feature engineering: Linear and nonlinear space transformation methods,
T. Li, G. Kou, Y. Peng, · 2020
Closest in time.
Transforming device fingerprinting for wireless security via online multitask metric learning,
Y. Luo, H. Hu, Y. Wen, D. Tao, · 2020
Closest in time.
Automatic speaker recognition with limited data,
R. Li, J.-Y. Jiang, J. L. Li, C.-C. Hsieh, W. Wang, · 2020
Closest in time.
Speaker verification by partial auc optimization with mahalanobis distance metric learning,
Z. Bai, X.-L. Zhang, J. Chen, · 2020
Closest in time.
Effective metric learning with co-occurrence embedding for collaborative recommendations,
H. Wu, Q. Zhou, R. Nie, J. Cao, · 2020
Closest in time.
A social recommendation based on metric learning and network embedding,
X. Li, Y. Tang, · 2020
Closest in time.
Similarity learning with joint transfer constraints for person re-identification,
C. Zhao, X. Wang, W. Zuo, F. Shen, L. Shao, D. Miao, · 2020
Closest in time.
Transfer learning and feature fusion for kinship verification,
F. Dornaika, I. Arganda-Carreras, O. Serradilla, · 2020
Closest in time.
Deep feature fusion through adaptive discriminative metric learning for scene recognition,
C. Wang, G. Peng, B. De Baets, · 2020
Closest in time.
Simple supervised dissimilarity measure: Bolstering iforest-induced similarity with class information without learning,
J. R. Wells, S. Aryal, K. M. Ting, · 2020
Closest in time.
Scalable large-margin distance metric learning using stochastic gradient descent,
B. Nguyen, C. Morell, B. De Baets, · 2020
Closest in time.
pydml: A python library for distance metric learning,
J. L. Suárez, S. García, F. Herrera, · 2020
Closest in time.
On the exact computation of the graph edit distance,
D. B. Blumenthal, J. Gamper, · 2020
Closest in time.
Discriminative deep metric learning for asymmetric discrete hashing,
L. Ma, H. Li, F. Meng, Q. Wu, K. N. Ngan, · 2020
Closest in time.
Improved deep embedding learning based on stochastic symmetric triplet loss and local sampling,
B. Nguyen, B. De Baets, · 2020
Closest in time.
Metric learning with submodular functions,
J. Pan, H. Le Capitaine, · 2020
Closest in time.
Metric learning for ordered labeled trees with pq -grams,
H. Shindo, M. Nishino, Y. Kobayashi, A. Yamamoto, · 2020
Closest in time.
Tensor cross-view quadratic discriminant analysis for kinship verification in the wild,
O. Laiadi, A. Ouamane, A. Benakcha, A. Taleb-Ahmed, A. Hadid, · 2020
Closest in time.