What's the meaning of dimensionality and what is it for this data?
May 5, 2015 · Dimensionality is the number of columns of data which is basically the attributes of data like name, age, sex and so on. While classification or clustering the data, we need to …
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 X. How does it work? What is the connection between these two approaches? …
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?
What should you do if you have too many features in your dataset ...
Aug 17, 2020 · Whereas dimensionality reduction removes unnecessary/useless data that generates noise. My main question is, if excessive features in a dataset could cause overfitting …
dimensionality reduction - How to reverse PCA and reconstruct …
Principal component analysis (PCA) can be used for dimensionality reduction. After such dimensionality reduction is performed, how can one approximately reconstruct the original …
dimensionality reduction - How To Determine The Number Of …
Apr 4, 2015 · Generally the dimensionality of the problem is, as you suspected, equal to the number of inputs ( also known as, features, measurement variables ). So in the NN model, that …
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 …
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 …
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? …
Intuitive explanation of how UMAP works, compared to t-SNE
Apr 12, 2019 · I have a PhD in molecular biology. My studies recently started to involve high dimensional data analysis. I got the idea of how t-SNE works (thanks to a StatQuest video on …