منابع مشابه
Bayesian Manifold Regression
There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors D is large, one encounters a daunting problem in attempting to estimate a D-dimensional surface based on limited data. Fortunately, in many applications, the support of the data is concentrated on a d-dimensional subspace with d ≪ D. Manifold learning attempts ...
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Nonparametric regression for massive numbers of samples (n) and features (p) is an important problem. We propose a Bayesian approach for scaling up Gaussian process (GP) regression to big n and p settings using random compression. The proposed compressed GP is particularly motivated by the setting in which features can be projected to a low-dimensional manifold with minimal loss of information ...
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Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a fixed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the highdimensional feat...
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Article history: Received 8 September 2008 Received in revised form 23 February 2009 Accepted 1 May 2009
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Laser-induced breakdown spectroscopy (LIBS) is currently being used on-board the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2016
ISSN: 0090-5364
DOI: 10.1214/15-aos1390