NDLI: Chunk incremental IDR/QR LDA learning

Content Provider	IEEE Xplore Digital Library
Author	Yiming Peng Shaoning Pang Gang Chen Sarrafzadeh, A. Tao Ban Inoue, D.
Copyright Year	2013
Description	Author affiliation: Nat. Inst. of Inf. & Commun. Technol., Koganei, Japan (Tao Ban; Inoue, D.) \|\| Dept. of Comput., Unitec Inst. of Technol., Auckland, New Zealand (Yiming Peng; Shaoning Pang; Gang Chen; Sarrafzadeh, A.)
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 $R^{d×n},$ instead of the entire incoming data chunk X ϵ $R^{d×s},$ 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.
Starting Page	1
Ending Page	8
File Size	935055
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6707018
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matrix decomposition Time complexity Face Data models Computational modeling Accuracy
Content Type	Text
Resource Type	Article

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