Incremental tensor principal component analysis for image recognition

被引：0
作者：
机构：
[1] College of Information Science and Technology, Chengdu University
[2] Key Laboratory of Pattern Recognition and Intelligent Information Processing, Sichuan Province
来源：
| 1600年 / Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland卷 / 710期
关键词：
Image recognition; Incremental learning; Principal component analysis; Tensor analysis;
D O I：
10.4028/www.scientific.net/AMR.710.584
中图分类号：
学科分类号：
摘要：
Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms. © (2013) Trans Tech Publications, Switzerland.
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收藏
页码：584 / 588
页数：4
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