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 …
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 …
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.
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? …
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 dimensions …
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 …
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
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 …
Can the elbow method be used in PCA (Principal ... - Cross Validated
May 16, 2025 · I’m wondering if a similar technique can be applied to PCA for dimensionality reduction. Specifically, can we use an "elbow" in the explained variance plot to determine the best number of …