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,...

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local bin...

We introduce the weighted mixed almost unbiased ridge estimator (WMAURE) based on the weighted mixed estimator (WME) (Trenkler and Toutenburg 1990) and the almost unbiased ridge estimator (AURE) (Akdeniz and Erol 2003) in linear regression model. We discuss superiorities of the new estimator under the quadratic bias (QB) and the mean square error matrix (MSEM) criteria. Additionally, we give a ...

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...