A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection

Abstract : Given a set of binary vectors drawn from a ¯nite multiple Bernoulli mixture model, an important problem is to determine which vectors are outliers and which features are relevant. The goal of this paper is to propose a model for binary vectors clustering that accommo- dates outliers and allows simultaneously the incorporation of a feature selection methodology into the clustering process. We derive an EM al- gorithm to ¯t the proposed model. Through simulation studies and a set of experiments involving handwritten digit recognition and visual scenes categorization, we demonstrate the usefulness and e®ectiveness of our method.
Complete list of metadatas

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-00647989
Contributor : Khalid Daoudi <>
Submitted on : Sunday, December 4, 2011 - 4:21:12 PM
Last modification on : Thursday, July 26, 2018 - 2:08:02 PM
Long-term archiving on: Monday, March 5, 2012 - 2:20:43 AM

File

Binaryoutliers.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00647989, version 1

Collections

Citation

Mohamed Al Mashrgy, Nizar Bouguila, Khalid Daoudi. A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection. International Conference on Neural Information Processing (ICONIP), Nov 2011, Shanghai, China. ⟨hal-00647989⟩

Share

Metrics

Record views

809

Files downloads

1245