https://www.zhihu.com/question/34143886/answer/196294308
奇异值分解的揭秘（二）：降维与奇异向量的意义
奇异值分解的揭秘（一）：矩阵的奇异值分解过程
浅谈张量分解（三）：如何对稀疏矩阵进行奇异值分解？
如何直观地理解「协方差矩阵」？
PCA(主成分分析)
奇异值分解（SVD）
奇异值的物理意义是什么？
https://www.zhihu.com/question/22237507/answer/53804902
https://zhuanlan.zhihu.com/p/21580949
http://colah.github.io/posts/2014-10-Visualizing-MNIST/
https://www.matongxue.com/madocs/491.html
https://arxiv.org/pdf/1404.1100.pdf
https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca
http://www.ams.org/publicoutreach/feature-column/fcarc-svd
What is the intuitive relationship between SVD and PCA -- a very popular and very similar thread on math.SE.
Why PCA of data by means of SVD of the data? -- a discussion of what are the benefits of performing PCA via SVD [short answer: numerical stability].
PCA and Correspondence analysis in their relation to Biplot -- PCA in the context of some congeneric techniques, all based on SVD.
Is there any advantage of SVD over PCA? -- a question asking if there any benefits in using SVD instead of PCA [short answer: ill-posed question].
Making sense of principal component analysis, eigenvectors & eigenvalues -- my answer giving a non-technical explanation of PCA. To draw attention, I reproduce one figure here:
http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/
https://zh.wikipedia.org/wiki/%E5%8D%8F%E6%96%B9%E5%B7%AE%E7%9F%A9%E9%98%B5
http://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/
http://www.visiondummy.com/2014/05/feature-extraction-using-pca/
http://www.visiondummy.com/2014/03/divide-variance-n-1/
http://www.visiondummy.com/2014/03/eigenvalues-eigenvectors/
http://www.visiondummy.com/2014/03/eigenvalues-eigenvectors/
http://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/
http://pinkyjie.com/2010/08/31/covariance/
https://en.wikipedia.org/wiki/Variance
https://deeplearning4j.org/eigenvector#a-beginners-guide-to-eigenvectors-pca-covariance-and-entropy
http://blog.csdn.net/watkinsong/article/details/8234766
http://blog.csdn.net/watkinsong/article/details/38536463
https://stats.stackexchange.com/questions/10251/what-is-the-objective-function-of-pca/10256#10256
主成分分析推导
https://www.cnblogs.com/Merodach/p/9033734.html
关于PCA的形象理解
https://zhuanlan.zhihu.com/p/29993872
http://www.cnblogs.com/LeftNotEasy/archive/2011/01/19/svd-and-applications.html
PCA （主成分分析）详解 （写给初学者） 结合matlab
https://my.oschina.net/gujianhan/blog/225241
如何理解矩阵特征值？
https://www.zhihu.com/question/21874816/answer/181864044
机器学习中的数学(5)-强大的矩阵奇异值分解(SVD)及其应用
主成份分析(PCA)最详细和全面的诠释