to prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the hughes phenomenon. a practical way to handle the hughes problem is preparing a lot of training samples until the size ...

The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem. The GCV criterion identifies model parameters for the blur, the image, and the regularization parameter, providing all the information ...

For practical engineering structures, it is usually difficult to measure external load distribution in a direct manner, which makes inverse identification important. Specifically, typical problem, for the models (e.g., response matrix) are often ill-posed, resulting degraded accuracy and impaired noise immunity of identification. This study aims at identifying loads stiffened plate structure, t...

New non-parametric regression procedures called BSML (Basis Selection from Multiple Libraries) are proposed in this paper for estimating a complex function by a linear combination of basis functions adaptively selected from multiple libraries. Different classes of basis functions are chosen to model various features of the function, e.g. truncated constants can model change points in the functi...

Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The regression function with two predictors in the non-parametric model will have two different non-parametric regression functions. Therefore, we...

Spline smoothing provides a powerful tool for estimating nonparametric functions. Most of the past work is based on the assumption that the random errors are independent. Observations are often correlated in applications; e.g., time series data, spatial data and clustered data. It is well known that correlation greatly a ects the selection of smoothing parameters, which are critical to the perf...

Adaptive choice of smoothing parameters for nonparametric Poisson regression (O’Sullivan et. al., 1986) is considered in this paper. A computable approximation of the unbiased risk estimate (AUBR) for Poisson regression is introduced. This approximation can be used to automatically tune the smoothing parameter for the penalized likelihood estimator. An alternative choice is the generalized appr...