qq_33790600的博客

“Real-world data, such as images of faces and digits, usually have a high dimension which leads to the well-known curse of dimensionality in statistical pattern recognition.”现实世界中的数据，如人脸和数字图像，通常具有很高的维数，这就导致了统计模式识别中众所周知的维数诅咒。“Various methods of dimensiona

“They just use each instance to reconstruct itself and ignore to explicitly model the data relation so as to discover the underlying effective manifold structure.”他们只是利用每个实例对自身进行重构，而忽略对数据关系的显式建模，从而发现底层有效的流形结构。...

(a) Principal component analysis as an exploratory tool for data analysis“We seek a linear combination of the columns of matrix X with maximum variance.”我们寻求一个方差最大的矩阵X的列的线性组合。

“Consider n variables attaching to each individual of a population. These statistical variables x~1 x2, . . . , xn”

“Its idea is simple—reduce the dimensionality of a dataset, while preserving as much ‘variability’ (i.e. statistical information) as possible.”它的想法很简单——降低数据集的维数，同时保留尽可能多的“可变性”(即统计信息)。“This means that ‘preserving as much variability as possible’ translate

“Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss.”