Results 51 to 60 of about 11,532 (157)
Overlapping sliced inverse regression for dimension reduction [PDF]
Analysis and Applications, 2019 Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In this paper, we propose to refine the SIR algorithm through an overlapping slicing scheme. The new algorithm, called overlapping SIR (OSIR)
Zhang, Ning, Yu, Zhou, Wu, Qiang
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Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning [PDF]
[[abstract]]Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data.
A.A. Alizadeh +61 more
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A Note on Sliced Inverse Regression with Regularizations [PDF]
Biometrics, 2008 Summary In Li and Yin (2008, Biometrics64, 124–131), a ridge SIR estimator is introduced as the solution of a minimization problem and computed thanks to an alternating least‐squares algorithm. This methodology reveals good performance in practice. In this note, we focus on the theoretical properties of the estimator.
Bernard-Michel, Caroline +2 more
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Asymptotics for sliced average variance estimation
, 2007 In this paper, we systematically study the consistency of sliced average variance estimation (SAVE). The findings reveal that when the response is continuous, the asymptotic behavior of SAVE is rather different from that of sliced inverse regression (SIR)
Li, Yingxing, Zhu, Li-Xing
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, 2014 Principal component analysis can be used as a dimension reduction method by discarding the components whose variance is below a given threshold. Projecting the model output on the low dimensional subspace thus determined preserves its most salient features. However, this only uses the information carried by the output data.
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Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression [PDF]
, 2008 International audienceIn the context of hyperspectral image analysis in planetology, we show how to estimate the physical parameters that generate the spectral infrared signal reflected by Mars. The training approach we develop is based on the estimation
Bernard-Michel, Caroline +3 more
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Online Sparse Sliced Inverse Regression
, 2020 Due to the demand for tackling the problem of streaming data with high dimensional covariates, we propose an online sparse sliced inverse regression (OSSIR) method for online sufficient dimension reduction. The existing online sufficient dimension reduction methods focus on the case when the dimension $p$ is small.
Cheng, Haoyang +2 more
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Averaging orthogonal projectors
, 2012 Dimensionality is a major concern in analyzing large data sets. Some well known dimension reduction methods are for example principal component analysis (PCA), invariant coordinate selection (ICS), sliced inverse regression (SIR), sliced average variance
Liski, Eero +3 more
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Determining the dimension of iterative Hessian transformation
, 2005 The central mean subspace (CMS) and iterative Hessian transformation (IHT) have been introduced recently for dimension reduction when the conditional mean is of interest. Suppose that X is a vector-valued predictor and Y is a scalar response.
Cook, R. Dennis, Li, Bing
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A note on the choice of the number of slices in sliced inverse regression [PDF]
, 2007 Sliced inverse regression (SIR) is a clever technique for reducing the dimension of the predictor in regression problems, thus avoiding the curse of dimensionality. There exist many contributions on various aspects of the performance of SIR. Up to now,
Becker, Claudia, Gather, Ursula
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