TY - GEN
T1 - A brief survey of dimension reduction
AU - Song, Li
AU - Ma, Hongbin
AU - Wu, Mei
AU - Zhou, Zilong
AU - Fu, Mengyin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Dimension reduction problem is a big concern which can reduce the scale of a database and keep the main features of these data simultaneously. This paper aims at reviewing and comparing different dimension reduction algorithms. Mainly, the performances of four basic algorithms (PCA, LDA, LLE and LE), their improved methods and deep learning methods are compared by reviewing the previous work. Their recognition accuracy and running time are carefully analyzed. We conclude that PCA and LDA are used more frequently in related fields. Combined methods usually perform better than original methods. Besides, deep learning method is also an approach developed in recent years, which outperforms existing traditional algorithms, though there are many barriers at present, such as obtaining huge labeled database, the computing and power limitation of different systems etc. Future research should focus on the processing of larger database. Finally, some new applications of dimension reduction are reviewed.
AB - Dimension reduction problem is a big concern which can reduce the scale of a database and keep the main features of these data simultaneously. This paper aims at reviewing and comparing different dimension reduction algorithms. Mainly, the performances of four basic algorithms (PCA, LDA, LLE and LE), their improved methods and deep learning methods are compared by reviewing the previous work. Their recognition accuracy and running time are carefully analyzed. We conclude that PCA and LDA are used more frequently in related fields. Combined methods usually perform better than original methods. Besides, deep learning method is also an approach developed in recent years, which outperforms existing traditional algorithms, though there are many barriers at present, such as obtaining huge labeled database, the computing and power limitation of different systems etc. Future research should focus on the processing of larger database. Finally, some new applications of dimension reduction are reviewed.
KW - Deep learning
KW - Dimension reduction (DR)
KW - LDA
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=85057092882&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-02698-1_17
DO - 10.1007/978-3-030-02698-1_17
M3 - Conference contribution
AN - SCOPUS:85057092882
SN - 9783030026974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 189
EP - 200
BT - Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers
A2 - Yu, Kai
A2 - Peng, Yuxin
A2 - Jiang, Xingpeng
A2 - Lu, Jiwen
PB - Springer Verlag
T2 - 8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018
Y2 - 18 August 2018 through 19 August 2018
ER -