Structured Sparse Additive Models
نویسندگان
چکیده
1.1 Parametric models: Linear Regression with non-linear basis functions Although the linear regression with linear basis is widely used in different areas, it is not powerful enough for lots of the real world cases as not all the models are linear in the real world. However, we can use non-linear basis functions to deal with non-linear relationships. It is just a linear combination of some function of x, φj(x).
منابع مشابه
Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...
متن کاملSparse Regularization for High Dimensional Additive Models
We study the behavior of the l1 type of regularization for high dimensional additive models. Our results suggest remarkable similarities and differences between linear regression and additive models in high dimensional settings. In particular, our analysis indicates that, unlike in linear regression, l1 regularization does not yield optimal estimation for additive models of high dimensionality....
متن کاملNormal-Mixture-of-Inverse-Gamma Priors for Bayesian Regularization and Model Selection in Structured Additive Regression Models
In regression models with many potential predictors, choosing an appropriate subset of covariates and their interactions at the same time as determining whether linear or more flexible functional forms are required is a challenging and important task. We propose a spike-and-slab prior structure in order to include or exclude single coefficients as well as blocks of coefficients associated with ...
متن کاملConvex-constrained Sparse Additive Modeling and Its Extensions
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. In this work we show how shape constraints such as convexity/concavity and their extensions, can be integrated into additive models. The proposed sparse difference of convex additive models (SDCAM) can estimate most continuous functions without any a priori smoothness assumption. M...
متن کاملSketching Structured Matrices for Faster Nonlinear Regression
Motivated by the desire to extend fast randomized techniques to nonlinear lp regression, we consider a class of structured regression problems. These problems involve Vandermonde matrices which arise naturally in various statistical modeling settings, including classical polynomial fitting problems, additive models and approximations to recently developed randomized techniques for scalable kern...
متن کامل