Name
Abstract | ||
|---|---|---|
Training data in real world is often presented in random chunks. Yet existing sequential Incremental IDR/QR LDA (s-QR/IncLDA) can only process data one sample after another. This paper proposes a constructive chunk Incremental IDR/QR LDA (c-QR/IncLDA) for multiple data samples incremental learning. Given a chunk of s samples for incremental learning, the proposed c-QR/IncLDA increments current discriminant model Ω, by implementing computation on the compressed the residue matrix Δ ϵ Rd×n, instead of the entire incoming data chunk X ϵ Rd×s, where η ≤ s holds. Meanwhile, we derive a more accurate reduced within-class scatter matrix W to minimize the discriminative information loss at every incremental learning cycle. It is noted that the computational complexity of c-QR/IncLDA can be more expensive than s-QR/IncLDA for single sample processing. However, for multiple samples processing, the computational efficiency of c-QR/IncLDA deterministically surpasses s-QR/IncLDA when the chunk size is large, i.e., s ≫ η holds. Moreover, experiments evaluation shows that the proposed c-QR/IncLDA can achieve an accuracy level that is competitive to batch QR/LDA and is consistently higher than s-QR/IncLDA. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IJCNN.2013.6707018 | IJCNN |
Keywords | Field | DocType |
discriminant model,learning (artificial intelligence),matrix algebra,multiple data samples incremental learning,s-qr-inclda,c-qr-inclda,sequential incremental idr-qr lda,random chunks,chunk incremental idr-qr lda learning,multiple samples processing,single sample processing,residue matrix,learning artificial intelligence | Multiple data,Pattern recognition,Computer science,Discriminant,Matrix (mathematics),Incremental learning,Artificial intelligence,Discriminative model,Machine learning,Scatter matrix,Computational complexity theory,Computation | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4673-6128-6 | 3 |
PageRank | References | Authors |
0.36 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiming Peng | 1 | 37 | 6.33 |
| Shaoning Pang | 2 | 711 | 52.69 |
| Gang Chen | 3 | 48 | 16.42 |
| Abdolhossein Sarrafzadeh | 4 | 134 | 22.64 |
| Tao Ban | 5 | 102 | 25.58 |
| Daisuke Inoue | 6 | 67 | 17.51 |