Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis
نویسندگان
چکیده
منابع مشابه
Regularized Sparse Kernel Slow Feature Analysis
This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent variables from time series. Generative relationships are usually complex, and current algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to provide a powerful function...
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Kernel Principal Component Analysis (KPCA) has proven to be a versatile tool for unsupervised learning, however at a high computational cost due to the dense expansions in terms of kernel functions. We overcome this problem by proposing a new class of feature extractors employing`1 norms in coeecient space instead of the reproducing kernel Hilbert space in which KPCA was originally formulated i...
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Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams, but recognizing dynamic texture is challenging due to the complex temporal variations. In this paper, we present a novel approach stemmed from slow feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly varying features from fast varying signals. Fortunately, SFA is capable to leach invariant repr...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2012
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-012-5300-0