Title :
The Performance Analysis of the Self-Stabilizing Douglas´s MCA Algorithm
Author :
Kong Xiangyu ; Hu Changhua ; Han ChongZhao
Author_Institution :
Xi´an Res. Inst. of High Technol., Xi´an, China
Abstract :
The minor component (MC) is the eigenvector associated with the smallest eigenvalue of the correlation matrix of input data. In many information processing areas, it is important to online extract MC from high-dimensional input data stream. Usually, MCA learning algorithms are described by stochastic discrete time (SDT) systems and the convergence is analyzed via a corresponding DCT system, but some restrictive conditions must be satisfied in this method. The SDT method use directly the stochastic discrete learning laws to analyze the temporal behavior of MCA algorithms and some important results can be obtained. In this paper, the theoretical analysis of Douglas´s algorithm for MCA is given by using two methods: deterministic continuous time (DCT) system and stochastic discrete time system. The results of computer simulations are given to confirm the theoretical results.
Keywords :
continuous time systems; discrete time systems; eigenvalues and eigenfunctions; learning (artificial intelligence); statistical analysis; stochastic systems; Douglas MCA algorithm; convergence; correlation matrix; deterministic continuous time system; eigenvalue; eigenvector; minor component analysis; statistical method; stochastic discrete learning; stochastic discrete time system; Algorithm design and analysis; Convergence; Data mining; Discrete cosine transforms; Discrete time systems; Eigenvalues and eigenfunctions; Information processing; Performance analysis; Stochastic processes; Stochastic systems;
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
DOI :
10.1109/CISP.2009.5303452
