| HAL-Inria | Publications, software ... of Inria's scientists |
Conference papers
High dimensional data clustering
Abstract : Clustering in high-dimensional spaces is a recurrent problem in many domains, for example in object recognition. High-dimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.
Cited literature [10 references]
https://hal.inria.fr/inria-00548591
Contributor : Thoth Team <>
Submitted on : Thursday, January 6, 2011 - 9:21:10 AM
Last modification on : Wednesday, May 19, 2021 - 3:37:37 PM
Long-term archiving on: : Thursday, April 7, 2011 - 2:30:38 AM
Contributor : Thoth Team <>
Submitted on : Thursday, January 6, 2011 - 9:21:10 AM
Last modification on : Wednesday, May 19, 2021 - 3:37:37 PM
Long-term archiving on: : Thursday, April 7, 2011 - 2:30:38 AM
File
paper_COMPSTAT06.pdf
Files produced by the author(s)