Sliced Inverse Regression for Dimension Reduction: Comment

1991 ◽  
Vol 86 (414) ◽  
pp. 336 ◽  
Author(s):  
John T. Kent
Keyword(s):  
Dimension Reduction ◽  
Sliced Inverse Regression ◽  
Inverse Regression

Sliced inverse regression for integrative multi-omics data analysis

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Yashita Jain ◽  
Shanshan Ding ◽  
Jing Qiu
Keyword(s):  
Data Analysis ◽  
Dimension Reduction ◽  
Sliced Inverse Regression ◽  
Source Analysis ◽  
Omics Data ◽  
Inverse Regression ◽  
Reduction Methods ◽  
Single Data ◽  
Supervised Dimension Reduction ◽  
Omics Data Analysis

Abstract Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.

scholarly journals Analysis of microarray right-censored data through fused sliced inverse regression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jae Keun Yoo
Keyword(s):  
Dimension Reduction ◽  
Censored Data ◽  
Sliced Inverse Regression ◽  
High Dimensional ◽  
Sufficient Dimension Reduction ◽  
Inverse Regression ◽  
Right Censored Data ◽  
Practical Advantage ◽  
Linear Projection ◽  
Lower Dimensional

Abstract Sufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits. Recently, a fused approach for SIR is proposed to relieve this weakness, which fuses the kernel matrices computed by the SIR application from various numbers of slices. In the paper, the fused SIR is applied to a large-p-small n regression of a high-dimensional microarray right-censored data to show its practical advantage over usual SIR application. Through model validation, it is confirmed that the fused SIR outperforms the SIR with any number of slices under consideration.

scholarly journals Kernel Sliced Inverse Regression: Regularization and Consistency

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Wu ◽  
Feng Liang ◽  
Sayan Mukherjee
Keyword(s):  
Dimension Reduction ◽  
Real Data ◽  
Sliced Inverse Regression ◽  
Inverse Regression ◽  
Computational Stability ◽  
Generalization Performance ◽  
Nonlinear Dimension Reduction ◽  
Nonlinear Dimension

Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. However, there are numeric, algorithmic, and conceptual subtleties in making the method robust and consistent. We apply two types of regularization in this framework to address computational stability and generalization performance. We also provide an interpretation of the algorithm and prove consistency. The utility of this approach is illustrated on simulated and real data.

scholarly journals Overlapping sliced inverse regression for dimension reduction

2019 ◽  
Vol 17 (05) ◽  
pp. 715-736
Author(s):  
Ning Zhang ◽  
Zhou Yu ◽  
Qiang Wu
Keyword(s):  
Dimension Reduction ◽  
Sliced Inverse Regression ◽  
Inverse Regression ◽  
Effective Dimension ◽  
Regression Curve ◽  
Effective Factors ◽  
Effective Dimension Reduction ◽  
Supervised Dimension Reduction ◽  
Derivatives Of ◽  
Significant Factors

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), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately. We show that such overlapping procedure has the potential to identify the information contained in the derivatives of the inverse regression curve, which helps to explain the superiority of OSIR. We also prove that OSIR algorithm is [Formula: see text]-consistent and verify its effectiveness by simulations and real applications.

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