论文标题
提高山脊回归的高维数据分类
Boosting Ridge Regression for High Dimensional Data Classification
论文作者
论文摘要
脊回归是一个已建立的回归估计器，可以方便地适应分类问题。一个令人信服的原因可能是脊回归发出封闭式解决方案，从而促进训练阶段。但是，在遇到高维问题的情况下，涉及反转正则协方差矩阵的封闭式解决方案相当昂贵。此类操作的高计算需求也使构建脊回归的集合难度。在本文中，我们考虑学习一个脊回归器的合奏，在该集合中，每个回归器都经过自己的随机投影子空间进行培训。子空间回归器后来通过自适应增强方法组合。基于五个高维分类问题的实验证明了该方法在学习时间方面的有效性，在某些情况下可以观察到改善的预测性能。
Ridge regression is a well established regression estimator which can conveniently be adapted for classification problems. One compelling reason is probably the fact that ridge regression emits a closed-form solution thereby facilitating the training phase. However in the case of high-dimensional problems, the closed-form solution which involves inverting the regularised covariance matrix is rather expensive to compute. The high computational demand of such operation also renders difficulty in constructing ensemble of ridge regressions. In this paper, we consider learning an ensemble of ridge regressors where each regressor is trained in its own randomly projected subspace. Subspace regressors are later combined via adaptive boosting methodology. Experiments based on five high-dimensional classification problems demonstrated the effectiveness of the proposed method in terms of learning time and in some cases improved predictive performance can be observed.
