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 $\mathbf X$. 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 and …
dimensionality reduction - How to reverse PCA and reconstruct original ...
Principal component analysis (PCA) can be used for dimensionality reduction. After such dimensionality reduction is performed, how can one approximately reconstruct the original variables/features ...
clustering - Which dimensionality reduction technique works well for ...
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 dimensionality of …
Variational Autoencoder − Dimension of the latent space
What do you call a latent space here? The dimensionality of the layer that outputs means and deviations, or the layer that immediately precedes that? It sounds like you're talking about the former.
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
What does 1x1 convolution mean in a neural network?
The most common use case for this approach is dimensionality reduction, i.e. typically M < N is used. Actually, I'm not quite sure if there are many use cases to increasing the dimensionality, because in …