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Linear dependency modeling for feature fusion
Andy J.H. Ma
*
,
Pong Chi YUEN
*
Corresponding author for this work
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
16
Citations (Scopus)
Overview
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Dive into the research topics of 'Linear dependency modeling for feature fusion'. Together they form a unique fingerprint.
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Computer Science
Linear Feature
100%
Feature Fusion
100%
Linear Dependency
100%
Linear Combination
66%
Posterior Probability
66%
Linear Classifier
66%
Experimental Result
33%
Synthetic Data
33%
Combination Method
33%
Level Classifier
33%
Keyphrases
Linear Dependence
100%
Feature Fusion
100%
Dependency Modeling
100%
Feature Dependency
50%
Linear Features
37%
Linear Combination
25%
Posterior Probability
25%
Linear Classifier
25%
Synthetic Data
12%
Combined Method
12%
Feature Level
12%
Imposter
12%
Modeling Techniques
12%
Feature Classifier
12%
Combination Property
12%
Fusion Process
12%
Mathematics
Probability Theory
100%
Linear Combination
100%
Feature Fusion
100%
Synthetic Data
50%
Optimal Model
50%