2015年5月5日 · I've been told that dimensionality is usually referred to attributes or columns of the dataset. But in this case, does it include Class1 and Class2? and does dimensionality mean, the …

2015年1月22日 · However, it can also be performed via singular value decomposition (SVD) of the data matrix $\mathbf X$. How does it work? What is the connection between these two approaches? …

2025年5月16日 · I’m wondering if a similar technique can be applied to PCA for dimensionality reduction. Specifically, can we use an "elbow" in the explained variance plot to determine the best …

2022年1月9日 · Why is dimensionality reduction used if it almost always reduces the explained variation? Ask Question Asked 4 years, 1 month ago Modified 3 years, 11 months ago

I cannot expound, but I believe I've heard what sound like three different versions of the curse: 1) higher dimensions mean an exponentially-increasing amount of work, and 2) in higher dimensions you will …

2014年5月20日 · I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high

2020年8月23日 · While I know that some of the classification techniques such as k-nearest neighbour classifier suffer from the curse of high dimensionality, I wonder does the same apply to the support …

2018年4月13日 · And Dimensionality reduction is also projection to a (hopefuly) meaningful space. But dimensionality reduction has to do so in a uninformed way -- it does not know what task you are …

I learned that it's common to do dimensionality reduction before clustering. But, is there any situation that it is better to do clustering first, and then do dimensionality reduction?

2015年12月11日 · The places I have been reading about dimensionality curse explain it in conjunction to kNN primarily, and linear models in general. I regularly see top rankers in Kaggle using thousands of …