Sciweavers

Share
APWEB
2005
Springer

An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data

10 years 8 months ago
An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data
The dramatic growth in the number and size of on-line information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Inter-class Scatter (IIS) algorithm. Unlike traditional batch learners, IIS learns from a stream of training data, not a set. IIS overcomes the inherent problem of some other incremental operations such as Incremental Principal Component Analysis (PCA) and Incremental Linear Discriminant Analysis (LDA). The experimental results on the synthetic datasets show that IIS performs as well as LDA and is more robust against noise. In addition, the experiments on the Reuters Corpus Volume 1 (RCV1) dataset show that IIS outperforms state-of-the-art Incremental Principal Component Analysis (IPCA) algorithm, a related algorithm, and Information Gain in efficiency and effectiveness respectively.
Jun Yan, QianSheng Cheng, Qiang Yang, Benyu Zhang
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where APWEB
Authors Jun Yan, QianSheng Cheng, Qiang Yang, Benyu Zhang
Comments (0)
books