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, …
dimensionality reduction - Relationship between SVD and PCA.
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? …
machine learning - Why is dimensionality reduction used if it …
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, …
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? …
Curse of dimensionality- does cosine similarity work better and if …
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 …
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 …
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 …
Difference between dimensionality reduction and clustering
Apr 29, 2018 · Most of the research papers and even the package creators for example hdbscan recommends dimensionality reduction before applying clustering esp. If the number of …
How do I know my k-means clustering algorithm is suffering from …
Aug 30, 2016 · Often enough, you run into much more severe problems of k-means earlier than the "curse of dimensionality". k-means can work on 128 dimensional data (e.g. SIFT color …
Why is dimensionality reduction always done before clustering?
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?