Reduced-rank multi-label classification
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DOI: 10.1007/s11222-015-9615-0
Abstract: Multi-label classification is a natural generalization of the classical binary classification for classifying multiple class labels. It differs from multi-class classification in that the multiple class labels are not exclusive. The key challenge is to… read more here.
Keywords: reduced rank; label classification; classification; multi label ... See more keywords
Hilbert space methods for reduced-rank Gaussian process regression
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DOI: 10.1007/s11222-019-09886-w
Abstract: This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in… read more here.
Keywords: reduced rank; gaussian process; covariance function;
Robust reduced-rank modeling via rank regression
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2016.08.009
Abstract: Abstract There are many applications in which several response variables are predicted with a common set of predictors. To take into account the possible correlations among the responses, estimators with restricted rank were introduced. However,… read more here.
Keywords: reduced rank; regression; via rank; rank regression ... See more keywords
Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications
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DOI: 10.1016/j.neuroimage.2016.08.027
Abstract: ABSTRACT We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging‐genetic studies to identify… read more here.
Keywords: reduced rank; high dimensional; regression; tensor ... See more keywords
Effective hardware implementation of Volterra filters based on reduced‐rank approaches
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DOI: 10.1049/el.2017.4776
Abstract: The focus of this Letter is on the development of a new effective approach for the hardware implementation of Volterra filters. The proposed approach is based on exploiting the different significance levels of the branches… read more here.
Keywords: reduced rank; implementation; implementation volterra; volterra filters ... See more keywords
Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models
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DOI: 10.1080/02331888.2018.1467420
Abstract: ABSTRACT Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are… read more here.
Keywords: reduced rank; rank multivariate; multivariate generalized; theory ... See more keywords
Estimation bias and bias correction in reduced rank autoregressions
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DOI: 10.1080/07474938.2017.1308065
Abstract: ABSTRACT This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias… read more here.
Keywords: reduced rank; bias bias; correction reduced; bias correction ... See more keywords
Sentinel-2 Sharpening Using a Reduced-Rank Method
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2906048
Abstract: Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60… read more here.
Keywords: resolution; reduced rank; sharpening using; using reduced ... See more keywords
Johansen’s Reduced Rank Estimator Is GMM
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DOI: 10.3390/econometrics6020026
Abstract: The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator… read more here.
Keywords: reduced rank; johansen reduced; gmm; rank estimator ... See more keywords
Dietary Patterns Derived from Reduced Rank Regression Are Associated with the 5-Year Occurrence of Metabolic Syndrome: Aichi Workers’ Cohort Study
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DOI: 10.3390/nu14153019
Abstract: The aim of the present study was to derive dietary patterns to explain variation in a set of nutrient intakes or in the measurements of waist circumference (WC) and fasting blood glucose (FBG) using reduced… read more here.
Keywords: dietary patterns; rank regression; reduced rank; metabolic syndrome ... See more keywords
Robust Sparse Reduced-Rank Regression with Response Dependency
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DOI: 10.3390/sym14081617
Abstract: In multiple response regression, the reduced rank regression model is an effective method to reduce the number of model parameters and it takes advantage of interrelation among the response variables. To improve the prediction performance… read more here.
Keywords: rank regression; regression; reduced rank; sparse ... See more keywords