dc.contributor.author |
Mukherjee, Abhisek |
|
dc.date.accessioned |
2022-01-21T08:09:54Z |
|
dc.date.available |
2022-01-21T08:09:54Z |
|
dc.date.issued |
2020-06 |
|
dc.identifier.citation |
37p. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10263/7246 |
|
dc.description |
Dissertation under the supervision of Dr. Arijit Ghosh & Dr. Arijit Bishnu |
en_US |
dc.description.abstract |
Coreset is an important tool to effectively extract information from large amount
of data by sampling only a few elements from it, without any substantial loss
of the actual information. An -coreset is defined as a weighted set C obtained
from an universe X, so that for any solution set Q for a problem (referred to as
a query in coreset literature), jCost(X;Q) Cost(C;Q)j Cost(X;Q).
Our work is an attempt to generalize the solution provided in the paper “k-
Means Clustering of Lines for Big Data” (Marom and Feldman, NIPS, 2019),
and explore if it is possible to extend to k-flats in Rd as well. Following the approach
used in the paper mentioned, we will attempt at building a deterministic
algorithm to compute an -coreset whose size is near logarithmic of the input
size for a j-dimensional affine subspace in Rd. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Statistical Institute, Kolkata |
en_US |
dc.relation.ispartofseries |
Dissertation;;2020;32 |
|
dc.subject |
The minimum enclosing ball (MEB) |
en_US |
dc.subject |
D2 sampling |
en_US |
dc.title |
On coreset construction for K-means clustering of flats and hyperplanes |
en_US |
dc.type |
Other |
en_US |