Joint sparse principal component regression with robust property

Qi, Kai; Tu, Jingwen; Yang, Hu

doi:10.1016/j.eswa.2021.115845

Cited by 7 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They propose to combine the previous projection learning models with linear regression into one objective function and develop one-stage methods. The typical ones include sparse principal component regression (SPCR) [31], joint SPCR (JSPCR) [32], orthogonal autoencoder regression (OAR) [33] etc. To simultaneously take the local geometrical structure and discriminative information into account, Lai et al [34] imposed the L 2,1 -norm on the projection and LPP term to extract robust and discriminative features and proposed a generalised robust regression (GRR) method.…”

Section: Introductionmentioning

confidence: 99%

Low‐rank preserving embedding regression for robust image feature extraction

Zhang

Long

Deng

et al. 2023

IET Computer Vision

View full text Add to dashboard Cite

Although low‐rank representation (LRR)‐based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low‐dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further investigated. Therefore, this paper proposes a novel low‐rank preserving embedding regression (LRPER) method by integrating LRR, linear regression, and projection learning into a unified framework. In LRPER, LRR can reveal the underlying structure information to strengthen the robustness of projection learning. The robust metric L2,1‐norm is employed to measure the low‐rank reconstruction error and regression loss for moulding the noise and occlusions. An embedding regression is proposed to make full use of the prior information for improving the discriminability of the learned projection. In addition, an alternative iteration algorithm is designed to optimise the proposed model, and the computational complexity of the optimisation algorithm is briefly analysed. The convergence of the optimisation algorithm is theoretically and numerically studied. At last, extensive experiments on four types of image datasets are carried out to demonstrate the effectiveness of LRPER, and the experimental results demonstrate that LRPER performs better than some state‐of‐the‐art feature extraction methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Low‐rank preserving embedding regression for robust image feature extraction

Zhang

Long

Deng

et al. 2023

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…It involves applying robust principal component analysis (PCA) to the high-dimensional data of the regressor, followed by employing a robust regression method to regress the response variables on the obtained scores. [12][13][14][15] Among the robust regression methods, M-estimation is commonly used, which employs iterative weighted least squares to estimate the regression coefficients. 16 The weights assigned to each sample are determined based on the residuals from the previous regression.…”

Section: Introductionmentioning

confidence: 99%

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa

Huang,

Chen,

Xie

et al. 2023

Anal. Methods

View full text Add to dashboard Cite

show abstract

“…In addition, Kawano proposed another method 26 based on the singular value decomposition for of the loss function of PCA in SPCR. Qi et al proposed JSPCR, 27 an extension of SPCR, to be applied to the outcomes of an outlier. A method for using multi‐block data related to SPCR has also been proposed 28‐30 .…”

Section: Introductionmentioning

confidence: 99%

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Yuki

Tanioka

Yadohisa

2022

Statistics in Medicine

View full text Add to dashboard Cite

We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.

show abstract

Joint sparse principal component regression with robust property

Cited by 7 publications

References 26 publications

Low‐rank preserving embedding regression for robust image feature extraction

Low‐rank preserving embedding regression for robust image feature extraction

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Contact Info

Product

Resources

About