Correspondence matching using kernel principal components analysis and label consistency constraints
نویسندگان
چکیده
منابع مشابه
Correspondence matching using kernel principal components analysis and label consistency constraints
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how kernel principal components analysis (kernel PCA) can be effectively used for solving the rigid point correspondence matching problem when the point-sets are subject to outliers and random position jitter. Specifically, w...
متن کاملPersian Handwriting Analysis Using Functional Principal Components
Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
متن کاملProbabilistic Analysis of Kernel Principal Components
This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (i...
متن کاملProbabilistic analysis of kernel principal components: mixture modeling, and classification
This paper presents a probabilistic approach to analyze kernel principal components by naturally combining in one treatment the theory of probabilistic principal component analysis and that of kernel principal component analysis. In this formulation, the kernel component enhances the nonlinear modeling power, while the probabilistic structure offers (i) a mixture model for nonlinear data struct...
متن کاملApproximations of the standard principal components analysis and kernel PCA
Principal component analysis (PCA) is a powerful technique for extracting structure from possibly highdimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to calculate eigenvalues and corresponding eigenvecto...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2006
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2005.05.013