基于高维Lasso惩罚线性回归的非凸惩罚Oracle性质及其算法优化研究与应用
Research and Application of Oracle Properties and Algorithm Optimization of Non-Convex Penalties Based on High-Dimensional Lasso-Penalized Linear Regression

在数据科学与机器学习蓬勃发展的今天，数据采集技术的飞速发展，数据维度急剧增加，然而样本数量的增长却相对缓慢，这使得高维数据处理面临严峻挑战。在这种高维数据环境下，传统的线性回归方法遭遇诸多困境，如经典的最小二乘法，当特征矩阵不满秩时，无法获得唯一解，而且各维度间的高度相关性或冗余信息会导致模型过拟合，泛化能力下降，这就是所谓的“维度诅咒”问题。同时，高维数据的处理对计算资源和存储空间要求极高，大大增加了模型训练的时间与成本。高维空间惩罚线性回归为解决这些难题提供了有效途径。其中，Lasso惩罚回归通过引入L1范数惩罚项，能够实现特征选择，使部分不重要的系数压缩至0，从而简化模型结构，在一定程度上缓解了高维数据带来的问题。然而，Lasso惩罚回归也存在局限性，例如其惩罚力度在参数较大时依然持续，可能导致重要参数的过度压缩，影响估计的准确性。非凸惩罚函数的出现为高维数据降维和数据筛选提供了更优的解决方案。相较于传统的Lasso惩罚，非凸惩罚函数如SCAD和MCP具有独特的优势。这些非凸惩罚函数在系数较小时，惩罚力度与Lasso类似，能够有效压缩不重要的系数；而当系数增大到一定程度后，惩罚力度会逐渐减弱甚至趋近于零，避免了对重要系数的过度压缩，从而实现更精准的变量选择。从理论上讲，非凸惩罚估计满足Oracle性质，即具有变量选择一致性和渐近正态性，这意味着在高维数据环境下，它能够更准确地识别出真正对响应变量有影响的特征变量，排除冗余和噪声特征的干扰。鉴于非凸惩罚函数在高维数据降维和数据筛选方面的显著优势，深入研究基于非凸惩罚的高维空间惩罚线性回归具有重要的理论意义和实践价值。本文将围绕其基本原理、算法实现、优化策略展开详细探讨，并通过数值模拟和实际案例分析，验证其在高维数据处理中的有效性，为相关领域的研究和应用提供有力的理论支持和实践指导。
With the booming development of data science and machine learning, data collection technology has advanced rapidly, leading to a sharp increase in data dimensions. However, the growth of sample sizes is relatively slow, which poses severe challenges to high-dimensional data processing. In this high-dimensional data environment, traditional linear regression methods encounter numerous difficulties. For example, the classical least-squares method cannot obtain a unique solution when the feature matrix is not of full column rank. Moreover, the high correlation or redundant information among dimensions can lead to overfitting of the model and a decline in generalization ability, which is the so-called “curse of dimensionality” problem. At the same time, processing high-dimensional data requires extremely high computational resources and storage space, greatly increasing the training time and cost of the model. Penalized linear regression in high-dimensional spaces provides an effective way to solve these problems. Among them, the Lasso-penalized regression can achieve feature selection by introducing an L1-norm penalty term, compressing some unimportant coefficients to 0, thus simplifying the model structure and alleviating the problems brought by high-dimensional data to a certain extent. However, the Lasso-penalized regression also has limitations. For example, its penalty strength continues even when the parameters are large, which may lead to over-compression of important parameters and affect the