论文标题
通过监督线性投影改善了通过扩散图的降低降低性降低
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection
论文作者
论文摘要
执行分类任务时，原始的高维特征通常包含冗余信息，并导致计算复杂性和过度拟合。在本文中，我们假设数据样本位于单个基础平滑歧管上，并使用成对本地核距离定义了类内和类间相似性。我们的目的是找到一个线性投影，以最大程度地提高级别的相似性并同时最大程度地减少阶层间相似性，以便基于标签信息的投影低维数据可以优化成对距离，这更适合于扩散映射以进行进一步的维度降低。在几个基准数据集上进行的数值实验表明，我们提出的方法能够提取低维的特征，这可以帮助我们实现更高的分类精度。
When performing classification tasks, raw high dimensional features often contain redundant information, and lead to increased computational complexity and overfitting. In this paper, we assume the data samples lie on a single underlying smooth manifold, and define intra-class and inter-class similarities using pairwise local kernel distances. We aim to find a linear projection to maximize the intra-class similarities and minimize the inter-class similarities simultaneously, so that the projected low dimensional data has optimized pairwise distances based on the label information, which is more suitable for a Diffusion Map to do further dimensionality reduction. Numerical experiments on several benchmark datasets show that our proposed approaches are able to extract low dimensional discriminate features that could help us achieve higher classification accuracy.
