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 $\mathbf X$. How does it work? What is the connection between these two …
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 …
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 …
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 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? …
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 …
clustering - Which dimensionality reduction technique works well …
Sep 10, 2020 · Which dimensionality reduction technique works well for BERT sentence embeddings? Ask Question Asked 4 years, 8 months ago Modified 3 years, 5 months ago
machine learning - What is a latent space? - Cross Validated
Dec 27, 2019 · In machine learning I've seen people using high dimensional latent space to denote a feature space induced by some non-linear data transformation which increases the …