Timeline for Is there a multi-Gaussian version of the Mahalanobis distance ?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 10, 2018 at 19:46 | answer | added | fuscans | timeline score: 2 | |
| Mar 20, 2018 at 16:12 | vote | accept | vphenix | ||
| May 11, 2016 at 8:55 | answer | added | vphenix | timeline score: 5 | |
| May 11, 2016 at 8:52 | comment | added | vphenix | Thanks for your responses. Actually, I want to use topological data analysis (TDA) to analyse a large amount of data and such techniques rely on "lenses" which are real valued functions used to described data. Based on this measure, a simplicial complex is built to describe the topological stricture of data. After a little research, I found what I was looking for, a paper called "Deriving cluster analytic distance functions from gaussian mixture models" which proposes Mahalanobis-like distance for GMMs. | |
| May 10, 2016 at 17:19 | comment | added | ttnphns | This might be a good question. If you explain why you need it. Why, for example, just a Euclidean distance won't suit... You see, Mahalanobis distance is Euclidean distance corrected for the "curvature" (so to speak) of the space induced by the correlatedness of the dimensions. With more than one gaussians, more than one "curved" alternative spaces exist. In this context, what is your purpose then? | |
| May 10, 2016 at 17:14 | comment | added | Mark L. Stone | What do you want to use your "distance" for, and at what phase of the modeling, "solution", or model results analysis process do you wish to use it? There are a lot of papers floating around addressing use of Mahalanobis Distance within multiple cluster situations, such as mixture models. A little Googling may serve you well. | |
| May 10, 2016 at 16:36 | history | asked | vphenix | CC BY-SA 3.0 |