Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2015.2391201 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
classification accuracy,computational complexity,incremental linear discriminant analysis (ilda),linear discriminant analysis (lda).,algorithm design and analysis,linear discriminant analysis,economics,linear systems | Data set,Linear system,Computer science,Uncorrelated,Artificial intelligence,QR decomposition,Algorithm design,Pattern recognition,Algorithm,Linear discriminant analysis,Triangular matrix,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
PP | 99 | 2162-2388 |
Citations | PageRank | References |
13 | 0.50 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Delin Chu | 1 | 24 | 2.72 |
| Li-Zhi Liao | 2 | 448 | 35.22 |
| Michael K. Ng | 3 | 395 | 42.26 |
| Xiaoyan Wang | 4 | 13 | 0.50 |