Kernel-based nonlinear canonical analysis and time reversibility
نویسندگان
چکیده
منابع مشابه
Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring
Effective monitoring of industrial processes provides many benefits. However, for dynamic processes with strong nonlinearity many existing techniques still cannot give satisfactory monitoring performance. This is evidenced by the well known Tennessee Eastman (TE) benchmark process, where some faults, e.g. Faults 3 and 9, have not been comfortably detected by almost all data-driven approaches pu...
متن کاملKernel Generalized Canonical Correlation Analysis
A classical problem in statistics is to study relationships between several blocks of variables. The goal is to find variables of one block directly related to variables of other blocks. The Regularized Generalized Canonical Correlation Analysis (RGCCA) is a very attractive framework to study such a kind of relationships between blocks. However, RGCCA captures linear relations between blocks an...
متن کاملSparse Kernel Canonical Correlation Analysis
We review the recently proposed method of Relevance Vector Machines which is a supervised training method related to Support Vector Machines. We also review the statistical technique of Canonical Correlation Analysis and its implementation in a Feature Space. We show how the technique of Relevance Vectors may be applied to the method of Kernel Canonical Correlation Analysis to gain a very spars...
متن کاملNonlinear kernel-based statistical pattern analysis
The eigenstructure of the second-order statistics of a multivariate random population can be inferred from the matrix of pairwise combinations of inner products of the samples. Therefore, it can be also efficiently obtained in the implicit, high-dimensional feature spaces defined by kernel functions. We elaborate on this property to obtain general expressions for immediate derivation of nonline...
متن کاملKernel-Based Nonlinear Independent Component Analysis
We propose the kernel-based nonlinear independent component analysis (ICA) method, which consists of two separate steps. First, we map the data to a high-dimensional feature space and perform dimension reduction to extract the effective subspace, which was achieved by kernel principal component analysis (PCA) and can be considered as a pre-processing step. Second, we need to adjust a linear tra...
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: Journal of Econometrics
سال: 2004
ISSN: 0304-4076
DOI: 10.1016/s0304-4076(03)00199-4