Title :
Deterministic Column-Based Matrix Decomposition
Author :
Li, Xuelong ; Pang, Yanwei
Author_Institution :
State Key Lab. of Transient Opt. & Photonics, Chinese Acad. of Sci., Xi´´an, China
Abstract :
In this paper, we propose a deterministic column-based matrix decomposition method. Conventional column-based matrix decomposition (CX) computes the columns by randomly sampling columns of the data matrix. Instead, the newly proposed method (termed as CX_D) selects columns in a deterministic manner, which well approximates singular value decomposition. The experimental results well demonstrate the power and the advantages of the proposed method upon three real-world data sets.
Keywords :
data mining; learning (artificial intelligence); sampling methods; singular value decomposition; data matrix training; data mining; deterministic column-based matrix decomposition; random data matrix sampling; singular value decomposition; Matrix decomposition; dimensionality reduction.; feature extraction; incremental learning;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2009.64
