Add to ORKGA high-dimensional regression space usually causes problems in nonlinear system identification.However, if the regression data are contained in (or spread tightly around) some manifold, thedimensionality can be reduced. This paper presents a use of dimension reduction techniques tocompose a two-step identification scheme suitable for high-dimensional identification problems withmanifold-valued regression data. Illustrating examples are also given
Modeling data generated by physiological systems is a crucial step in many problems such as classifi...
This paper presents a novel nonparametric approach to the identification of nonlinear dynamical syst...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...
A high-dimensional regression space usually causes problems in nonlinear system identification.Howeve...
High-dimensional regression problems are becoming more and more common with emerging technologies. H...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
We study the problem of discovering a manifold that best preserves information relevant to a nonline...
The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same a...
Large scale dynamical systems (e.g. many nonlinear coupled differential equations)can often be summa...
High-dimensional gray-box identification is a fairly unexplored part of system identification. Never...
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
Abstract — Understanding the structure of multidimensional patterns, especially in unsupervised case...
Because of variable dependence, high dimensional data typically have much lower intrinsic dimensiona...
In this paper, we present a simulation study to investigate the role of manifold regularization in k...
Modeling data generated by physiological systems is a crucial step in many problems such as classifi...
This paper presents a novel nonparametric approach to the identification of nonlinear dynamical syst...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...
A high-dimensional regression space usually causes problems in nonlinear system identification.Howeve...
High-dimensional regression problems are becoming more and more common with emerging technologies. H...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
We study the problem of discovering a manifold that best preserves information relevant to a nonline...
The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same a...
Large scale dynamical systems (e.g. many nonlinear coupled differential equations)can often be summa...
High-dimensional gray-box identification is a fairly unexplored part of system identification. Never...
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
Abstract — Understanding the structure of multidimensional patterns, especially in unsupervised case...
Because of variable dependence, high dimensional data typically have much lower intrinsic dimensiona...
In this paper, we present a simulation study to investigate the role of manifold regularization in k...
Modeling data generated by physiological systems is a crucial step in many problems such as classifi...
This paper presents a novel nonparametric approach to the identification of nonlinear dynamical syst...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...