Skip to main navigation
Skip to search
Skip to main content
New Jersey Institute of Technology Home
Help & FAQ
Home
Profiles
Research units
Facilities
Federal Grants
Research output
Press/Media
Search by expertise, name or affiliation
A Novel Locally Linear KNN Method with Applications to Visual Recognition
Qingfeng Liu,
Chengjun Liu
Research output
:
Contribution to journal
›
Article
›
peer-review
61
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'A Novel Locally Linear KNN Method with Applications to Visual Recognition'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Computer Science
Approximation (Algorithm)
100%
Objective Function
100%
Sparsity
100%
Regularization
100%
Face Recognition
100%
Feature Extraction
100%
k-Nearest Neighbors Algorithm
100%
Object Recognition
100%
Bayes Decision Rule
100%
Action Recognition
100%
Power Transformation
100%
Recognition Performance
100%
Computational Efficiency
100%
Sparse Representation
100%
Engineering
Computational Efficiency
100%
Objective Function
100%
Regularization
100%
Marginals
100%
Feature Extraction
100%
Sparsity
100%
Decision Rule
100%
Nearest Neighbor
100%
Object Recognition
100%
Keyphrases
Visual Recognition
100%
Locally Linear
100%
KNN Method
100%
Novel Representation
33%
Computational Efficiency
16%
Further Processing
16%
Feature Extraction
16%
Face Recognition
16%
Sparsity
16%
Increased Reliability
16%
Bayes Rule
16%
Object Recognition
16%
Power Transformation
16%
Scene Recognition
16%
Minimum Error
16%
Shift Power
16%
Recognition Scene
16%
Action Recognition
16%
Recognition Performance
16%
Function-based
16%
Sparse Representation
16%
K-nearest Neighbor (K-NN)
16%
Nearest Mean
16%
Nonnegative Constraint
16%
Marginal Fisher Analysis
16%
Group Regularization
16%