منابع مشابه
Projection Pursuit Density Estimation
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متن کاملEfficient Parametric Projection Pursuit Density Estimation
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the "under complete product of experts" (UPoE), where each expert models a one dimensional pro jection of the data. The UPoE may be inter preted as a parametric probabilistic model for projection pursuit. Its ML learning rules are identical to the approximate learning rules proposed ...
متن کاملProjection Pursuit via Decomposition of Bias Termsof Kernel Density
Dimension reduction of data, < d ! < p (p << d), to be used for clustering has speciic requirements that are not generally met by generic dimension reduction algorithms such as principal components. Projection pursuit, on the other hand, has a growing variety of criteria that target holes, skewness, etc., using information measures, density functionals, sample moments, etc. With the exception o...
متن کاملFunctional Projection Pursuit
This article describes the adaption of exploratory projection pursuit for use with functional data. The aim is to nd \interesting" projections of functional data: e.g. to separate curves into meaningful clusters. Functional data are projected onto low-dimensional subspaces determined by a projection function using a suitable inner product. Such a projection is rapidly computed by representing d...
متن کاملOblique Projection Matching Pursuit
Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 1984
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.1984.10478086