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- Publisher Website: 10.1109/TNNLS.2015.2391201
- Scopus: eid_2-s2.0-85027942391
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Article: Incremental Linear Discriminant Analysis: A Fast Algorithm and Comparisons
Title | Incremental Linear Discriminant Analysis: A Fast Algorithm and Comparisons |
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Authors | |
Keywords | Classification accuracy linear discriminant analysis (LDA). incremental linear discriminant analysis (ILDA) computational complexity |
Issue Date | 2015 |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2015, v. 26, n. 11, p. 2716-2735 How to Cite? |
Abstract | © 2012 IEEE. It has always been a challenging task to develop a fast and an efficient incremental linear discriminant analysis (ILDA) algorithm. For this purpose, we conduct a new study for linear discriminant analysis (LDA) in this paper and develop a new ILDA algorithm. We propose a new batch LDA algorithm called LDA/QR. LDA/QR is a simple and fast LDA algorithm, which is obtained by computing the economic QR factorization of the data matrix followed by solving a lower triangular linear system. The relationship between LDA/QR and uncorrelated LDA (ULDA) is also revealed. Based on LDA/QR, we develop a new incremental LDA algorithm called ILDA/QR. The main features of our ILDA/QR include that: 1) it can easily handle the update from one new sample or a chunk of new samples; 2) it has efficient computational complexity and space complexity; and 3) it is very fast and always achieves competitive classification accuracy compared with ULDA algorithm and existing ILDA algorithms. Numerical experiments based on some real-world data sets demonstrate that our ILDA/QR is very efficient and competitive with the state-of-the-art ILDA algorithms in terms of classification accuracy, computational complexity, and space complexity. |
Persistent Identifier | http://hdl.handle.net/10722/276547 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chu, Delin | - |
dc.contributor.author | Liao, Li Zhi | - |
dc.contributor.author | Ng, Michael Kwok Po | - |
dc.contributor.author | Wang, Xiaoyan | - |
dc.date.accessioned | 2019-09-18T08:33:56Z | - |
dc.date.available | 2019-09-18T08:33:56Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2015, v. 26, n. 11, p. 2716-2735 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276547 | - |
dc.description.abstract | © 2012 IEEE. It has always been a challenging task to develop a fast and an efficient incremental linear discriminant analysis (ILDA) algorithm. For this purpose, we conduct a new study for linear discriminant analysis (LDA) in this paper and develop a new ILDA algorithm. We propose a new batch LDA algorithm called LDA/QR. LDA/QR is a simple and fast LDA algorithm, which is obtained by computing the economic QR factorization of the data matrix followed by solving a lower triangular linear system. The relationship between LDA/QR and uncorrelated LDA (ULDA) is also revealed. Based on LDA/QR, we develop a new incremental LDA algorithm called ILDA/QR. The main features of our ILDA/QR include that: 1) it can easily handle the update from one new sample or a chunk of new samples; 2) it has efficient computational complexity and space complexity; and 3) it is very fast and always achieves competitive classification accuracy compared with ULDA algorithm and existing ILDA algorithms. Numerical experiments based on some real-world data sets demonstrate that our ILDA/QR is very efficient and competitive with the state-of-the-art ILDA algorithms in terms of classification accuracy, computational complexity, and space complexity. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | Classification accuracy | - |
dc.subject | linear discriminant analysis (LDA). | - |
dc.subject | incremental linear discriminant analysis (ILDA) | - |
dc.subject | computational complexity | - |
dc.title | Incremental Linear Discriminant Analysis: A Fast Algorithm and Comparisons | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2015.2391201 | - |
dc.identifier.scopus | eid_2-s2.0-85027942391 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 2716 | - |
dc.identifier.epage | 2735 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000363242800009 | - |
dc.identifier.issnl | 2162-237X | - |