In linear algebra, the singular value decomposition (SVD) is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics. Applications which employ the SVD include computing the pseudoinverse, least squares fitting of data, matrix approximation, and determining the rank, range and null space of a matrix.
Published in Chapter:
The Clustering of Large Scale E-Learning Resources
Fei Wu (Zhejiang University, China), Wenhua Wang (Zhejiang University, China), Hanwang Zhang (Zhejiang University, China), and Yueting Zhuang (Zhejiang University, China)
Copyright: © 2010
|Pages: 11
DOI: 10.4018/978-1-60566-380-7.ch006
Abstract
E-learning resources increase vastly with the pervasion of the Internet. Thus, the retrieval of e-learning resources becomes more and more important. This chapter introduces an approach to retrieve e-learning resources from large-scale dataset. The basic idea behind that method is, the authors cluster the whole resources into topics first, and only search from those clusters which are the most tightly relevant to the query. To make the clustering feasible to large-scale dataset, the authors adapt affinity propagation in MapReduce framework and therefore the so called parallel affinity propagation is proposed. The proposed approach could improve the retrieval of e-learning resources by understanding users’ underlying intentions.