What's the meaning of dimensionality and what is it for this data?
May 5, 2015 · For instance, Class1 could legitimately be replaced by two columns, in which one indicates whether "MIT" is the value and a second one indicates whether "NUC" is the value. …
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?
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 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 - 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 …
What is embedding? (in the context of dimensionality reduction)
Sep 15, 2020 · 6 In the context of dimensionality reduction one often uses word embedding, which seems to me a rather technical mathematical term, which rather stands out compared to …
Would PCA work for boolean (binary) data types?
Jul 3, 2015 · Short answer: linear PCA (if it is taken as dimensionality reduction technique and not latent variable technique as factor analysis) can be used for scale (metrical) or binary data. …
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 …
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 …
time series dimensionality reduction - Cross Validated
My question was about dimensionality reduction and forecasting directly to handle data that was collected every 15 minutes as opposed to a top down approach like the one you are suggestion.