Latent Feature Models
Latent Feature Models is a type of latent variable model that represents observed data as generated from a set of underlying, non-observable features, where each feature can be actively present or absent in individual data instances, thereby allowing overlapping feature memberships and capturing complex data structures through probabilistic assignments.
431
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Citations
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Authors
489
Institutions
Publications per year
2017–2026
287
Parents
431
Domain Generalization with Adversarial Feature Learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang et al. · 2018 · 1.2K citations
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Ondřej Chum, James Philbin, Josef Šivic et al. · 2007 · 865 citations
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Chinese Academy of Sciences
Beijing, China
15 h-index
41 publications
2.8K citations
Tsinghua University
Beijing, China
13 h-index
32 publications
3.9K citations
Stanford University
Stanford, United States
11 h-index
25 publications
3.9K citations
Nanyang Technological University
Singapore, Singapore
11 h-index
23 publications
5.2K citations

Harbin Institute of Technology
Harbin, China
11 h-index
31 publications
1.5K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
18 publications
798 citations

Northwestern Polytechnical University
Xi'an, China
9 h-index
25 publications
1.7K citations
University of Cambridge
Cambridge, United Kingdom
9 h-index
23 publications
1K citations
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Repository · 40 publications · 1.6K citations
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Conference · 11 publications · 431 citations
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12 publications · 509 citations
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TPAMI · 9 publications · 996 citations
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8 publications · 1K citations
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