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Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training.
A generalized solution of the orthogonal procrustes problem
Peter H Schönemann · 1966
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Probabilistic principal component analysis
Michael E Tipping and Christopher M Bishop · 1999
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Canonical correlation analysis: An overview with application to learning methods
David R. Hardoon, Sándor Szedmák, and John Shawe-Taylor · 2004
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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Alexandr Andoni and Piotr Indyk · 2006
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Practical solutions to the problem of diagonal dominance in kernel document clustering
Derek Greene and Pádraig Cunningham · 2006
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Spectral hashing
Yair Weiss, Antonio Torralba, and Robert Fergus · 2008
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Learning from multiple partially observed views - an application to multilingual text categorization
Massih-Reza Amini, Nicolas Usunier, and Cyril Goutte · 2009
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Nus-wide: A real-world web image database from national university of singapore
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yan-Tao. Zheng · 2009
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Locality-sensitive binary codes from shift-invariant kernels
Maxim Raginsky and Svetlana Lazebnik · 2009
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A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Iterative quantization: A procrustean approach to learning binary codes
Yunchao Gong and Svetlana Lazebnik · 2011
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A graph-based framework for multi-task multi-view learning
Jingrui He and Rick Lawrence · 2011
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Learning hash functions for cross-view similarity search
Shaishav Kumar and Raghavendra Udupa · 2011
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Hashing with graphs
Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang · 2011
Cited alongside, same era.
Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang · 2011
Cited alongside, same era.
Learning using privileged information: SVM+ and weighted SVM
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2014
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Inductive transfer deep hashing for image retrieval
Xinyu Ou, Lingyu Yan, Hefei Ling, Cong Liu, and Maolin Liu · 2014
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Hybrid heterogeneous transfer learning through deep learning
Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W. Tsang, and Yan Yan · 2014
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Heterogeneous domain adaptation for multiple classes
Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, and Mingkui Tan · 2014
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Learning using privileged information: Similarity control and knowledge transfer
Vladimir Vapnik and Rauf Izmailov · 2015
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Quantized correlation hashing for fast cross-modal search
Botong Wu, Qiang Yang, Wei-Shi Zheng, Yizhou Wang, and Jingdong Wang · 2015
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Composite hashing with multiple information sources
Dan Zhang, Fei Wang, and Luo Si · 2011
Cited alongside, same era.
Learning to rank using privileged information
Viktoriia Sharmanska, Novi Quadrianto, and Christoph H. Lampert · 2013
Cited alongside, same era.
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Jingkuan Song, Yang Yang, Yi Yang, Zi Huang, and Heng Tao Shen · 2013
Cited alongside, same era.
Density sensitive hashing
Zhongming Jin, Cheng Li, Yue Lin, and Deng Cai · 2014
Cited alongside, same era.
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Distance metric learning using privileged information for face verification and person re-identification
Xinxing Xu, Wen Li, and Dong Xu · 2015
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Action and event recognition in videos by learning from heterogeneous web sources
Li Niu, Xinxing Xu, Lin Chen, Lixin Duan, and Dong Xu · 2016
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Learning to hash for indexing big data - A survey
Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang · 2016
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Transfer learning for cross-language text categorization through active correspondences construction
Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W. Tsang, and Shen-Shyang Ho · 2016
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