摘 要
随着数据规模的不断扩大和复杂度的日益增加，机器学习领域面临着高维数据处理的巨大挑战。本文聚焦于机器学习中的特征选择与降维技术，旨在深入探讨其理论基础、算法实现及应用效果。研究通过对比分析多种经典方法如主成分分析（PCA）、线性判别分析（LDA）等，并引入新型稀疏表示和深度自编码器模型，提出了一种融合多源信息的混合降维框架。该框架不仅能够有效保留原始数据结构特征，还实现了对冗余特征的精准剔除。针对特征选择过程中的稳定性问题，创新性地引入了基于遗传算法的优化策略，进一步提高了特征子集的选择鲁棒性。本研究为解决高维数据处理难题提供了新的思路和技术支持，对于推动机器学习在实际场景中的广泛应用具有重要价值。
关键词：特征选择与降维 高维数据处理 混合降维框架 稀疏表示
Abstract
With the continuous expansion of data scale and increasing complexity, the field of machine learning faces great challenges in high-dimensional data processing. This paper focuses on feature selection and dimensionality reduction in machine learning, and aims to deeply discuss its theoretical basis, algorithm implementation and application effects. By comparing and analyzing several classical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), and introducing a new sparse representation and deep autoencoder model, a hybrid dimensionality reduction fr amework combining multi-source information is proposed. The fr amework can not only effectively retain the original data structure features, but also realize the precise elimination of redundant features. In order to solve the stability problem of feature selection, an optimization strategy based on genetic algorithm is innovatively introduced to further improve the robustness of feature subset selection. This research provides new ideas and technical support for solving the problem of high-dimensional data processing, and has important value for promoting the wide application of machine learning in practical scenarios.
Keyword:Feature Selection And Dimensionality Reduction High-Dimensional Data Processing Hybrid Dimensionality Reduction fr amework Sparse Representation
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
2特征选择方法综述 1
2.1特征选择的基本概念 2
2.2过滤式特征选择方法 2
2.3包裹式特征选择方法 3
2.4嵌入式特征选择方法 3
3降维技术原理与应用 4
3.1降维技术的数学基础 4
3.2线性降维方法分析 4
3.3非线性降维方法探讨 5
3.4降维技术的应用场景 5
4特征选择与降维的融合策略 6
4.1融合策略的理论依据 6
4.2基于特征选择的降维优化 6
4.3基于降维的特征选择改进 7
4.4实验验证与结果分析 7
结论 8
参考文献 9
致谢 10
