降维在机器学习和模式识别领域中起着至关重要的作用.目前,现有的基于投影的方法往往只单一地利用了数据之间的距离信息或表示关系来保持数据的结构,难以有效捕捉高维空间中数据流形的非线性特征和复杂相关性.为了解决这个问题,本文提出了一种利用潜在稀疏表示学习的增强局部保持投影(enhanced locality preserving projection with latent sparse representation learning,LPP_SRL)方法.所提出方法不仅利用距离信息以保留数据的局部结构,而且利用多重局部线性表示来揭示数据的全局非线性结构.此外,为了在投影学习和稀疏自表示之间建立联系,本文采用了一种新策略,将稀疏自表示中的字典替换为低维表示的重构样本.通过这种方法,能够有效地过滤掉不相关的特征和噪声,从而更好地保留原始特征空间中的主要成分.在多个公开可用的基准数据集上进行的大量实验证明了所提出方法的有效性和优越性.
Abstract
Dimensionality reduction plays a crucial role in machine learning and pattern recognition.The existing projection-based methods tend to solely utilize distance information or representation relationships among data points to maintain the data structure,which makes it difficult to effectively capture the nonlinear features and complex correlations of data manifolds in high-dimensional space.To address this issue,this study proposes a method:enhanced locality preserving projection with latent sparse representation learning(LPP_SRL).The method not only utilizes distance information to preserve the local structure of the data but also leverages multiple local linear representations to unveil the global nonlinear structure of the data.Moreover,to establish a connection between projection learning and sparse self-representation,this study employs a novel strategy by replacing the dictionary in sparse self-representation with reconstructed samples from the low-dimensional representation.This approach effectively filters out irrelevant features and noise,thereby better preserving the principal components in the original feature space.Extensive experiments conducted on multiple publicly available benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.
维普
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