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I understand what is "curse of dimensionality", and I have done some high dimensional optimization problems and know the challenge of the exponential possibilities. … (In most books "curse of dimensionality" says dimension > 10 could be problematic. In their demos they use uniform distribution in all dimensions, where entropy is really high. …

I heard many times about curse of dimensionality, but somehow I'm still unable to grasp the idea, it's all foggy. … How would the increase of dimensionality make the job of clustering their toys harder? …

I have seen it many times in a number of articles that nonparametric techniques are subject to the curse of dimensionality, which may lead to the failure of these methods. Why does this happen? …

From "] Learning Deep Architectures for AI". see: http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf (see section 3.1) "The local generalization issue is directly connected to the literature on the curse … of dimensionality, but the results we cite show that what matters for generalization is not dimensionality, but instead the number of “variations” of the function we wish to obtain after learning. …

After having read about the curse of dimensionality, I have been looking into a filtered version of the Superconductivity dataset. … Is there any possible situation where reducing the dimensionality of the data lead to a increase of the prediction power? …

I have been looking at the following notebook: time series clustering where the writer says that the dataset is affected by the "Curse of Dimensionality", so applying TimeSeriesKMeans offered by tslearn … The dataset in the article has only 2 features (timestamp, value) and many data, so in my opinion, there is no "Curse of dimensionality", hence doing the PCA on all the "value" columns is useless. …

UMAP is touted as an excellent dimensionality reduction technique by constructing a high-dimensional topological graph, and then reconstructing said graph in low dimensional space. … If we're using UMAP to escape the curse of dimensionality by collapsing some of the feature space, why are we uses KNN to do this, a technique which suffers directly from the curse of dimensionality? …

I would like some clarification as to how principal component analysis mitigates the Curse of Dimensionality problem. … How have I evaded the Curse of Dimensionality? It is really not obvious to me. …

The curse of dimensionality states that in high dimension every distance between pairs of points tends to be the same. See this answer for more details. …

The curse of dimensionality refers to the fact that the huge number of correlated features tends to increase the complexity of the treatment that has to be applied to the data set. …

I'm dealing with high dimensional text data, and I want to do feature reduction to help avoid the curse of dimensionality, but don't Random Forests already to some sort of dimension reduction? …

(1) First point where the curse of dimensionality starts to bother us: Where exactly the breaking down (vanishing) of KDE prevents us from having good clusters? …

Question Is the covariance between high-dimensional vectors less accruate than covariance between two vectors in low-dimensional vecotrs? … We have 10 day-long vectors(10 dimensional) 2 vectors above. …

Are there any guidelines for knowing how many training samples are needed based on the number of features you have in order to not have accuracy degradation as a result of the curse of dimensionality? …

I feel like the dimensionality is affecting my results - is there a way to check if it's true? Do I need to have several examples for each combination of the feature values? …