.png){kind=icon}
.png){kind=icon}
Return
A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2784800.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the traditional graph embedding framework, the graph is usually built by k-NN or r-ball. Since it is difficult to manually set the parameters k and r in the high-dimensional space, sparse representation-based methods are usually introduced to automatically build the graphs. In recent years, nuclear norm-based matrix regression (NMR) has been proposed for face recognition using the low rank structural information (i.e., the image matrix-based error model). Inspired by NMR, we give a NMR-based projections (NMRP) method for feature extraction and recognition. The experiments on FERET and extended Yale B face databases show that NMR can be used to build the graph while NMRP is an effective feature extraction method.
Keywords:
NMR
NMRP
graph embedding
feature extraction
face recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
View more organizations