FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2908709000> ?p ?o ?g. }

• W2908709000 endingPage "118" @default.
• W2908709000 startingPage "105" @default.
• W2908709000 abstract "Feature fusion aims to provide enhancements of data authenticity in both traditional and deep learning pattern analysis. Canonical correlation analysis (CCA) based feature fusion is a main technique for exploring the mutual relationships of multiple feature sets. In traditional CCA-based feature fusion, the dimensionality of each feature set is usually first reduced using principal component analysis (PCA), linear discriminant analysis (LDA) etc. to ensure non-singularity and invertibility of covariance matrices. One issue with the above standard CCA-based feature fusion is that the reduced feature sets generated by PCA or LDA may neglect certain correlation information among different feature sets which is useful for CCA, and this in turn may degrade the following classification performance. Another issue is that most CCA fused features may still contain redundancies due to the correlation criterion. These redundancies may be relevant or irrelevant to class labels. The irrelevant redundancies may degrade the pattern recognition performance, while the relevant redundancies can make the pattern recognition system more robust. In this paper, we propose an enhanced feature fusion scheme through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis (IEDCA-IRE) addressing the above two issues. IEDCA-IRE explores the intra-class correlation including both the pairs-wise correlation like CCA-based feature fusion approaches and the correlation across different features within the same class. By incorporating kernelized IEDCA into minimum redundancy maximum relevance (mRMR) criterion, only the relevant redundancy is retained in the fused feature. Our proposed IEDCA-IRE can be used in unimodal feature fusion, multimodal feature fusion, fusion of deep features extracted from different deep neural network models as well as fusion of deep features and handcrafted features. Extensive experiments have proved its effectiveness." @default.
• W2908709000 created "2019-01-25" @default.
• W2908709000 creator A5029222157 @default.
• W2908709000 creator A5063565261 @default.
• W2908709000 creator A5066203581 @default.
• W2908709000 date "2019-03-01" @default.
• W2908709000 modified "2023-10-17" @default.
• W2908709000 title "Enhanced feature fusion through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis" @default.
• W2908709000 cites W1133916940 @default.
• W2908709000 cites W1951319388 @default.
• W2908709000 cites W1963631516 @default.
• W2908709000 cites W1975077471 @default.
• W2908709000 cites W1997242674 @default.
• W2908709000 cites W2001903799 @default.
• W2908709000 cites W2008340903 @default.
• W2908709000 cites W2025531964 @default.
• W2908709000 cites W2033310064 @default.
• W2908709000 cites W2045907009 @default.
• W2908709000 cites W2053101950 @default.
• W2908709000 cites W2081681829 @default.
• W2908709000 cites W2086384421 @default.
• W2908709000 cites W2105809105 @default.
• W2908709000 cites W2117539524 @default.
• W2908709000 cites W2124807296 @default.
• W2908709000 cites W2131987814 @default.
• W2908709000 cites W2132379769 @default.
• W2908709000 cites W2136885397 @default.
• W2908709000 cites W2154053567 @default.
• W2908709000 cites W2335720172 @default.
• W2908709000 cites W2405680777 @default.
• W2908709000 cites W2411377185 @default.
• W2908709000 cites W2515131597 @default.
• W2908709000 cites W2538244214 @default.
• W2908709000 cites W2538546808 @default.
• W2908709000 cites W2543969232 @default.
• W2908709000 cites W2554078916 @default.
• W2908709000 cites W2576754561 @default.
• W2908709000 cites W2618530766 @default.
• W2908709000 cites W2754845163 @default.
• W2908709000 cites W2757736749 @default.
• W2908709000 cites W2758333383 @default.
• W2908709000 cites W2772515592 @default.
• W2908709000 cites W2775609985 @default.
• W2908709000 cites W2883457751 @default.
• W2908709000 cites W3100063120 @default.
• W2908709000 doi "https://doi.org/10.1016/j.neucom.2019.01.029" @default.
• W2908709000 hasPublicationYear "2019" @default.
• W2908709000 type Work @default.
• W2908709000 sameAs 2908709000 @default.
• W2908709000 citedByCount "11" @default.
• W2908709000 countsByYear W29087090002019 @default.
• W2908709000 countsByYear W29087090002020 @default.
• W2908709000 countsByYear W29087090002021 @default.
• W2908709000 countsByYear W29087090002022 @default.
• W2908709000 countsByYear W29087090002023 @default.
• W2908709000 crossrefType "journal-article" @default.
• W2908709000 hasAuthorship W2908709000A5029222157 @default.
• W2908709000 hasAuthorship W2908709000A5063565261 @default.
• W2908709000 hasAuthorship W2908709000A5066203581 @default.
• W2908709000 hasBestOaLocation W29087090002 @default.
• W2908709000 hasConcept C111030470 @default.
• W2908709000 hasConcept C111919701 @default.
• W2908709000 hasConcept C117220453 @default.
• W2908709000 hasConcept C138885662 @default.
• W2908709000 hasConcept C152124472 @default.
• W2908709000 hasConcept C153180895 @default.
• W2908709000 hasConcept C153874254 @default.
• W2908709000 hasConcept C154945302 @default.
• W2908709000 hasConcept C2524010 @default.
• W2908709000 hasConcept C27438332 @default.
• W2908709000 hasConcept C2776401178 @default.
• W2908709000 hasConcept C33923547 @default.
• W2908709000 hasConcept C41008148 @default.
• W2908709000 hasConcept C41895202 @default.
• W2908709000 hasConcept C69738355 @default.
• W2908709000 hasConcept C70518039 @default.
• W2908709000 hasConcept C97931131 @default.
• W2908709000 hasConceptScore W2908709000C111030470 @default.
• W2908709000 hasConceptScore W2908709000C111919701 @default.
• W2908709000 hasConceptScore W2908709000C117220453 @default.
• W2908709000 hasConceptScore W2908709000C138885662 @default.
• W2908709000 hasConceptScore W2908709000C152124472 @default.
• W2908709000 hasConceptScore W2908709000C153180895 @default.
• W2908709000 hasConceptScore W2908709000C153874254 @default.
• W2908709000 hasConceptScore W2908709000C154945302 @default.
• W2908709000 hasConceptScore W2908709000C2524010 @default.
• W2908709000 hasConceptScore W2908709000C27438332 @default.
• W2908709000 hasConceptScore W2908709000C2776401178 @default.
• W2908709000 hasConceptScore W2908709000C33923547 @default.
• W2908709000 hasConceptScore W2908709000C41008148 @default.
• W2908709000 hasConceptScore W2908709000C41895202 @default.
• W2908709000 hasConceptScore W2908709000C69738355 @default.
• W2908709000 hasConceptScore W2908709000C70518039 @default.
• W2908709000 hasConceptScore W2908709000C97931131 @default.
• W2908709000 hasLocation W29087090001 @default.
• W2908709000 hasLocation W29087090002 @default.
• W2908709000 hasOpenAccess W2908709000 @default.
• W2908709000 hasPrimaryLocation W29087090001 @default.

• next