Linked Questions
36 questions linked to/from Why is Euclidean distance not a good metric in high dimensions?
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Need more intuition for the curse of dimensionality [duplicate]
People despise using Eucliean distance in higher dimensional spaces because it is not a viable metric. People argue that the distance between two vectors becomes very large as the number of dimensions ...
user46925
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KNN Distance measures [duplicate]
Why in KNN Euclidean distance is preferred over Manhattan for low dimension dataset whereas for high dimension, manhattan is preferred?
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Why do we need exponentially more data for an accurate prediction when working in a higher dimensional space? [duplicate]
I am a bit confused by the phenomenon of the curse of dimensionality. Most lecturers motivate this with the KNN classifier and I understand why higher dimensions should be avoided with this classifier ...
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Explain "Curse of dimensionality" to a child
I heard many times about curse of dimensionality, but somehow I'm still unable to grasp the idea, it's all foggy.
Can anyone explain this in the most intuitive way, as you would explain it to a child,...
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Euclidean distance is usually not good for sparse data (and more general case)?
I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
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How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?
I believe that the title of this question says it all.
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Why is dimensionality reduction always done before clustering? [duplicate]
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?
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Where are most points in a uniformly distributed high-dimensional ball?
Should they be close to the middle (origin) or close its surface?
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Is K-means performance a bottleneck everywhere?
I've read a paper about a sped-up version of k-means:
Ding et al. (2015). Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup.
Now I wonder, is k-means' ...
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A valid distance metric for high dimensional data
I asked a question about forming a valid distance metric yesterday (Link1) and got some very good answer; however, I have got some more questions about forming a proper distance metric for high ...
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Why is the curse of dimensionality also called the empty space phenomenon?
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. This is also called ...
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What is high dimensional data in data mining?
Currently I am studying effect of high dimensions of data on clustering , for experiment purpose I want to use kdd dataset from UCI which contains 42 features.
Is kdd a high dimensional data or what ...
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Standardizing some features in K-Means
I have 21 features in my dataset, some features are more important than others. As a fact I know, if I don't standardize (mean=0, SD=1) any features, then features with low variance will have slightly ...
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Distance measure methods of R function dist() evaluation
I want to compute the distance matrix for the columns on a 1000 x 230 matrix using the dist() function in R. Though, I am uncertain about which method to use.
I ...
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How to choose an internal clustering evaluation metric?
I am trying to cluster textual data using fastText vectors with different clustering algorithms, mainly K-Means and DBSCAN.
I would like to know which internal evaluation metric works best with K-...
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