论文标题
线性维度降低
Linear Dimensionality Reduction
论文作者
论文摘要
这些注释是多元数据分析中某些经典线性方法的概述。这是一个很好的旧领域，自60年代以来就建立了良好的领域，并及时刷新了统计学习的关键一步。它可以作为统计学习的一部分表示，也可以作为几何风味降低维度。两种方法都是紧密相连的：从低维空间中的数据中学习模式比在高维空间中更容易。可以显示多种方法和工具如何归结为具有SVD的PCA的单个核心方法，以便优化分析大量数据集（例如分布式内存和基于任务的编程）的代码，或提高算法效率（如随机SVD）的效率，可以专注于这种共享的核心方法，并利用所有方法。
These notes are an overview of some classical linear methods in Multivariate Data Analysis. This is a good old domain, well established since the 60's, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with a geometric flavor. Both approaches are tightly linked: it is easier to learn patterns from data in low dimensional spaces than in high-dimensional spaces. It is shown how a diversity of methods and tools boil down to a single core methods, PCA with SVD, such that the efforts to optimize codes for analyzing massive data sets like distributed memory and task-based programming or to improve the efficiency of the algorithms like Randomised SVD can focus on this shared core method, and benefit to all methods.
