论文标题
具有自适应邻居的监督歧视性稀疏PCA可降低维度
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
论文作者
论文摘要
减少维度是信息可视化，特征提取，聚类，回归和分类的重要操作，尤其是用于处理噪声高维数据。但是，大多数现有方法都保留了数据的全球或局部结构，但并非两者兼而有之。仅保留全球数据结构的方法，例如主成分分析（PCA），通常对异常值敏感。仅保留本地数据结构（例如保留局部数据预测）的方法通常是无监督的（因此无法使用标签信息），并使用固定的相似性图。我们提出了一种新型的线性降低方法，将有监督的稀疏PCA与适应性邻居（SDSPCAAN）（SDSPCAAN）相结合，以将无社区的监督歧视性稀疏PCA和预计的聚类与适应性邻居相结合。结果，全局和本地数据结构以及标签信息都用于更好地降低维度。对九个高维数据集进行的分类实验验证了我们提出的SDSPCAAN的有效性和鲁棒性。
Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.
