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, …
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 - 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 …
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 …
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 …
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, …
What're the differences between PCA and autoencoder?
Oct 15, 2014 · Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another?
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 …
dimensionality reduction - How can UMAP improve HDBSCAN …
Jun 13, 2025 · I went through UMAPs official documentation which says HDBSCAN, being a density based algorithm suffers from curse of dimensionality and reducing dimensions with …
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 …