Mean-Field Approaches to Independent Component Analysis
نویسندگان
چکیده
منابع مشابه
Nonlinear Approaches To Independent Component Analysis
Recently, there has been a great interest in statistical models for learning data representations. A popular method for this task is Independent Component Analysis (ICA) which has been successfully applied for the blind separation of mixed sounds and the analysis of biomedical signals. ICA relies on strong assumptions such as the linear mixing model, the requirement that the number of sensors a...
متن کاملExpectation-Maximization approaches to independent component analysis
Expectation–Maximization (EM) algorithms for independent component analysis are presented in this paper. For super-Gaussian sources, a variational method is employed to develop an EM algorithm in closed form for learning the mixing matrix and inferring the independent components. For sub-Gaussian sources, a symmetrical form of the Pearson mixture model (Neural Comput. 11 (2) (1999) 417–441) is ...
متن کاملRank based Least-squares Independent Component Analysis
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...
متن کاملMean field and fluid approaches to Markov chain analysis
Representing the explicit state space of performance models has inherent difficulties. Just as the state-space explosion effects functional correctness evaluation, so it can also be easily a problem in performance models. In particular, classical Markov chain analysis of any variety requires exploration of the global state space and, even for a simple system, this quickly becomes computationall...
متن کاملNeural approaches to independent component analysis and source separation
Independent Component Analysis (ICA) is a recently developed technique that in many cases characterizes the data in a natural way. The main application area of the linear ICA model is blind source separation. Here, unknown source signals are estimated from their unknown linear mixtures using the strong assumption that the sources are mutually independent. In practice, separation can be achieved...
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: Neural Computation
سال: 2002
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976602317319009