What's the meaning of dimensionality and what is it for this data?
May 5, 2015 · 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 …
dimensionality reduction - Relationship between SVD and PCA. How to …
Jan 22, 2015 · However, it can also be performed via singular value decomposition (SVD) of the data matrix X. How does it work? What is the connection between these two approaches? What is the …
machine learning - Why is dimensionality reduction used if it almost ...
Jan 9, 2022 · So, the dimensionality reduction (ignoring years) is clearly best. However, if it turns out that you are in an inflationary periods, not so good monthly seasonal adjustment. However, a year …
Curse of dimensionality- does cosine similarity work better and if so ...
Apr 19, 2018 · When working with high dimensional data, it is almost useless to compare data points using euclidean distance - this is the curse of dimensionality. However, I have read that using …
Why is t-SNE not used as a dimensionality reduction technique for ...
Apr 13, 2018 · 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 …
Why is Euclidean distance not a good metric in high dimensions?
May 20, 2014 · 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
Does Dimensionality curse effect some models more than others?
Dec 11, 2015 · 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 …
Explain "Curse of dimensionality" to a child - Cross Validated
Aug 28, 2015 · The curse of dimensionality is that in higher dimensions, one either needs a much larger neighborhood for a given number of observations (which makes the notion of locality questionable) …
Does SVM suffer from curse of high dimensionality? If no, Why?
Aug 23, 2020 · 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 …
What are the implications of the curse of dimensionality for ordinary ...
Nov 1, 2016 · I'm trying to determine how the number of data points needed for a statistically significant estimate in the context of an ordinary least squares linear regression varies with respect to the …