Matches in SemOpenAlex for { <https://semopenalex.org/work/W2422804631> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W2422804631 abstract "Sparse feature (dictionary) selection is critical for various tasks in computer vision, machine learning, and pattern recognition to avoid overfitting. While extensive research efforts have been conducted on feature selection using sparsity and group sparsity, we note that there has been a lack of development on applications where there is a particular preference on diversity. That is, the selected features are expected to come from different groups or categories. This diversity preference is motivated from many real-world applications such as advertisement recommendation, privacy image classification, and design of survey. In this paper, we proposed a general bilevel exclusive sparsity formulation to pursue the diversity by restricting the overall sparsity and the sparsity in each group. To solve the proposed formulation that is NP hard in general, a heuristic procedure is proposed. The main contributions in this paper include: 1) A linear convergence rate is established for the proposed algorithm, 2) The provided theoretical error bound improves the approaches such as L1 norm and L0 types methods which only use the overall sparsity and the quantitative benefits of using the diversity sparsity is provided. To the best of our knowledge, this is the first work to show the theoretical benefits of using the diversity sparsity, 3) Extensive empirical studies are provided to validate the proposed formulation, algorithm, and theory." @default.
- W2422804631 created "2016-06-24" @default.
- W2422804631 creator A5000721267 @default.
- W2422804631 creator A5049225160 @default.
- W2422804631 creator A5051451613 @default.
- W2422804631 creator A5069376593 @default.
- W2422804631 creator A5074948475 @default.
- W2422804631 date "2016-06-01" @default.
- W2422804631 modified "2023-09-27" @default.
- W2422804631 title "On Benefits of Selection Diversity via Bilevel Exclusive Sparsity" @default.
- W2422804631 cites W1566135517 @default.
- W2422804631 cites W1977520307 @default.
- W2422804631 cites W1980454827 @default.
- W2422804631 cites W1994238794 @default.
- W2422804631 cites W2027922120 @default.
- W2422804631 cites W2028633780 @default.
- W2422804631 cites W2039051707 @default.
- W2422804631 cites W2041861657 @default.
- W2422804631 cites W2052044664 @default.
- W2422804631 cites W2054667730 @default.
- W2422804631 cites W2089749945 @default.
- W2422804631 cites W2095978736 @default.
- W2422804631 cites W2109363337 @default.
- W2422804631 cites W2116148865 @default.
- W2422804631 cites W2116498084 @default.
- W2422804631 cites W2129131372 @default.
- W2422804631 cites W2129812935 @default.
- W2422804631 cites W2135046866 @default.
- W2422804631 cites W2138019504 @default.
- W2422804631 cites W2138265962 @default.
- W2422804631 cites W2140514146 @default.
- W2422804631 cites W2141204169 @default.
- W2422804631 cites W2151103935 @default.
- W2422804631 cites W2158917775 @default.
- W2422804631 cites W2159400887 @default.
- W2422804631 cites W2161969291 @default.
- W2422804631 cites W2289917018 @default.
- W2422804631 cites W3100535899 @default.
- W2422804631 cites W3104148512 @default.
- W2422804631 cites W3105340263 @default.
- W2422804631 cites W4250589301 @default.
- W2422804631 doi "https://doi.org/10.1109/cvpr.2016.640" @default.
- W2422804631 hasPublicationYear "2016" @default.
- W2422804631 type Work @default.
- W2422804631 sameAs 2422804631 @default.
- W2422804631 citedByCount "3" @default.
- W2422804631 countsByYear W24228046312017 @default.
- W2422804631 countsByYear W24228046312018 @default.
- W2422804631 countsByYear W24228046312020 @default.
- W2422804631 crossrefType "proceedings-article" @default.
- W2422804631 hasAuthorship W2422804631A5000721267 @default.
- W2422804631 hasAuthorship W2422804631A5049225160 @default.
- W2422804631 hasAuthorship W2422804631A5051451613 @default.
- W2422804631 hasAuthorship W2422804631A5069376593 @default.
- W2422804631 hasAuthorship W2422804631A5074948475 @default.
- W2422804631 hasConcept C11413529 @default.
- W2422804631 hasConcept C137836250 @default.
- W2422804631 hasConcept C154945302 @default.
- W2422804631 hasConcept C17744445 @default.
- W2422804631 hasConcept C199539241 @default.
- W2422804631 hasConcept C2781316041 @default.
- W2422804631 hasConcept C3309286 @default.
- W2422804631 hasConcept C41008148 @default.
- W2422804631 hasConcept C81917197 @default.
- W2422804631 hasConceptScore W2422804631C11413529 @default.
- W2422804631 hasConceptScore W2422804631C137836250 @default.
- W2422804631 hasConceptScore W2422804631C154945302 @default.
- W2422804631 hasConceptScore W2422804631C17744445 @default.
- W2422804631 hasConceptScore W2422804631C199539241 @default.
- W2422804631 hasConceptScore W2422804631C2781316041 @default.
- W2422804631 hasConceptScore W2422804631C3309286 @default.
- W2422804631 hasConceptScore W2422804631C41008148 @default.
- W2422804631 hasConceptScore W2422804631C81917197 @default.
- W2422804631 hasLocation W24228046311 @default.
- W2422804631 hasOpenAccess W2422804631 @default.
- W2422804631 hasPrimaryLocation W24228046311 @default.
- W2422804631 hasRelatedWork W1990422213 @default.
- W2422804631 hasRelatedWork W2013586784 @default.
- W2422804631 hasRelatedWork W2085949256 @default.
- W2422804631 hasRelatedWork W2606073172 @default.
- W2422804631 hasRelatedWork W2948307425 @default.
- W2422804631 hasRelatedWork W2970622411 @default.
- W2422804631 hasRelatedWork W4224078567 @default.
- W2422804631 hasRelatedWork W4291418339 @default.
- W2422804631 hasRelatedWork W4321472378 @default.
- W2422804631 hasRelatedWork W2751504412 @default.
- W2422804631 isParatext "false" @default.
- W2422804631 isRetracted "false" @default.
- W2422804631 magId "2422804631" @default.
- W2422804631 workType "article" @default.