The SSIR-PACS is a group identification and model-free variable selection method under sufficient dimension reduction (SDR) settings. It combined the Pairwise Absolute Clustering Sparsity (PACS) with sliced inverse regression (SIR) methods to produce solutions sparsity ability of identification. However, depends on classical estimates for dispersion location, squared loss function, non-robust w...

MOTIVATION We introduce simple graphical classification and prediction tools for tumor status using gene-expression profiles. They are based on two dimension estimation techniques sliced average variance estimation (SAVE) and sliced inverse regression (SIR). Both SAVE and SIR are used to infer on the dimension of the classification problem and obtain linear combinations of genes that contain su...

We propose a novel approach to sufficient di­ mension reduction in regression, based on es­ timating contour directions of negligible vari­ ation for the response surface. These di­ rections span the orthogonal complement of the minimal space relevant for the regression, and can be extracted according to a mea­ sure of the variation in the response, lead­ ing to General Contour Regression (GCR)...

In this paper, the estimation of physical properties from hyperspectral data with support vectors machines is addressed. Several kernel functions are used, from classical to advanced ones. The results are compared with Gaussian Regularized Sliced Inversion Regression and Partial Least Squares, both in terms of accuracy and complexity. Experiments on simulated data show that SVM produce highly a...

In this paper we study the support recovery problem for single index models Y = f(Xβ, ε), where f is an unknown link function, X ∼ Np(0, Ip) and β is an s-sparse unit vector such that βi ∈ {± 1 s , 0}. In particular, we look into the performance of two computationally inexpensive algorithms: (a) the diagonal thresholding sliced inverse regression (DT-SIR) introduced by Lin et al. (2015); and (b...

In this paper, we propose a new method for supervised motion segmentation based on spatial-frequential analysis and dimension reduction techniques. A sequence of images could contain non-ridge motion in the region of interest and the segmentation of these moving objects with deformation is challenging. The aim is to extract feature vectors that capture the spatialfrequential information in the ...

Whilst influence functions for linear discriminant analysis (LDA) have been found a single when dealing with two groups, until now these not derived in the setting of general number groups. In this paper we explore relationship between Sliced Inverse Regression (SIR) and LDA, exploit to develop LDA from those already SIR. These can be used understand robustness properties also detect influentia...

Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe topological properties of complicated shapes. PDs enjoy strong stability properties and have proven their utility in various learning contexts. They do not, however, live in a space naturally endowed with a Hilbert structure and are usually compared with non-Hilbertian dis...

Among methods to analyze high-dimensional data, the sliced inverse regression (SIR) is of particular interest for non-linear relations between the dependent variable and some indices of the covariate. When the dimension of the covariate is greater than the number of observations, classical versions of SIR cannot be applied. Various upgrades were then proposed to tackle this issue such as RSIR a...

When analyzing multivariate data, one can appeal to the procedures of dimension reduction to describe its main features in the easiest way possible. In this thesis we work with one such methods, the sliced inverse regression (SIR), and propose a new adaptation to survival data. A popular idea to account for censoring is to reweight the observed data points, often with the help of inverse probab...