Post Nonlinear Independent Subspace Analysis
نویسندگان
چکیده
In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm.
منابع مشابه
Nonlinear Independent Factor Analysis by Hierarchical Models
The building blocks introduced earlier by us in [1] are used for constructing a hierarchical nonlinear model for nonlinear factor analysis. We call the resulting method hierarchical nonlinear factor analysis (HNFA). The variational Bayesian learning algorithm used in this method has a linear computational complexity, and it is able to infer the structure of the model in addition to estimating t...
متن کاملIndependent dynamics subspace analysis
The paper presents an algorithm for identifying the independent subspace analysis model based on source dynamics. We propose to separate subspaces by decoupling their dynamic models. Each subspace is extracted by minimizing the prediction error given by a first-order nonlinear autoregressive model. The learning rules are derived from a cost function and implemented in the framework of denoising...
متن کاملSeparation theorem for independent subspace analysis and its consequences
Independent component analysis (ICA) the theory of mixed, independent, non-Gaussian sources has a central role in signal processing, computer vision and pattern recognition. One of the most fundamental conjectures of this research eld is that independent subspace analysis (ISA) the extension of the ICA problem, where groups of sources are independent can be solved by traditional ICA followed by...
متن کاملForward kinematic analysis of planar parallel robots using a neural network-based approach optimized by machine learning
The forward kinematic problem of parallel robots is always considered as a challenge in the field of parallel robots due to the obtained nonlinear system of equations. In this paper, the forward kinematic problem of planar parallel robots in their workspace is investigated using a neural network based approach. In order to increase the accuracy of this method, the workspace of the parallel robo...
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 کامل