Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Juliang Hua"'
Publikováno v:
PLoS ONE, Vol 11, Iss 8, p e0159945 (2016)
In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructe
Externí odkaz:
https://doaj.org/article/5aa86e6b174148168911c67efe91e625
Publikováno v:
International Journal of Machine Learning and Cybernetics. 9:969-978
Data classification is a fundamental problem in many research areas. This paper proposes a novel classifier, namely local mean representation based classifier (LMRC), for data classification. Based on the concept that neighboring samples should have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c6a62a2ac576f3c178d1e8c515103d0
https://doi.org/10.1007/s13042-016-0621-0
https://doi.org/10.1007/s13042-016-0621-0
Publikováno v:
Neurocomputing. 193:1-6
This paper develops a collaborative representation reconstruction based projections (CRRP) method for dimension reduction. Collaborative representation based classification (CRC) is much faster than sparse representation based classification (SRC) wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f71c250d772849b3a24318303119fef5
Publikováno v:
Neural Computing and Applications. 28:225-231
Recently, sparse representation (SR) theory gets much success in the fields of pattern recognition and machine learning. Many researchers use SR to design classification methods and dictionary learning via reconstruction residual. It was shown that c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b956d2db9d317535c529c000c3443132
https://doi.org/10.1007/s00521-016-2299-3
https://doi.org/10.1007/s00521-016-2299-3
Publikováno v:
PLoS ONE, Vol 11, Iss 8, p e0159945 (2016)
PLoS ONE
PLoS ONE
In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65209eb1794e80c41d13828687f7f093
http://europepmc.org/articles/PMC4985152?pdf=render
http://europepmc.org/articles/PMC4985152?pdf=render