Subspace clustering methods have been extensively studied in recent years. For 2-dimensional (2D) data, existing subspace usually convert 2D examples to vectors, which severely damages inherent structural information and relationships of the original data. In this paper, we propose a novel method, named KTRR, for The KTRR provides us with way learn most representative features from data learnin...

A nonparametric regression model proposed in [Pelletier and Frouin, Applied Optics, 2006] as a solution to the geophysical problem of ocean color remote sensing is studied. The model, called ridge function field, combines a regression estimate in the form of a superposition of ridge functions, or equivalently a neural network, with the idea pertaining to varyingcoefficients models, where the pa...

Hawkins and Yin (Comput. Statist. Data Anal. 40 (2002) 253) describe an algorithm for ridge regression of reduced rank data, i.e. data where p, the number of variables, is larger than n, the number of observations. Whereas a direct implementation of ridge regression in this setting requires calculations of order O(np2 + p3), their algorithm uses only calculations of order O(np2). In this paper,...

We consider the application of a popular penalised regression method, Ridge Regression, to data with very high dimensions and many more covariates than observations. Our motivation is the problem of out-of-sample prediction and the setting is high-density genotype data from a genome-wide association or resequencing study. Ridge regression has previously been shown to offer improved performance ...

We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method (PCA-OLS) is within a constant...

This paper considers optimization of the ridge parameters in generalized ridge regression (GRR) by minimizing a model selection criterion. GRR has a major advantage over ridge regression (RR) in that a solution to the minimization problem for one model selection criterion, i.e., Mallows’ Cp criterion, can be obtained explicitly with GRR, but such a solution for any model selection criteria, e.g...

Diuretic activity [ p(1/C)] of benzene sulfonamides was modeled using 13 C NMR chemical shift ( as a molecular descriptor. The regression analyses were carried out using regular as well as Ridge multiple regression analyses. Application of variety of statistics namely ( statistics, Ridge regression and parameter derived there were used for modeling the diuretic activity. Results have shown that...