Extension of the Rocchio Classification Method to Multi-Modal Categorization of Documents in Social Media
Abstract
Most of the approaches in multi-view categorization use early fusion, late fusion or co-training strategies. We propose here a novel classification method that is able to efficiently capture the interactions across the different modes. This method is a multi-modal extension of the Rocchio classification algorithm – very popular in the Information Retrieval community. The extension consists of simultaneously maintaining different “centroid” representations for each class, in particular “cross-media” centroids that correspond to pairs of modes. To classify new data points, different scores are derived from similarity measures between the new data point and these different centroids; a global classification score is finally obtained by suitably aggregating the individual scores. This method outperforms the multi-view logistic regression approach (using either the early fusion or the late fusion strategies) on a social media corpus - namely the ENRON email collection - on two very different categorization tasks (folder classification and recipient prediction).
Cite
Text
Mantrach and Renders. "Extension of the Rocchio Classification Method to Multi-Modal Categorization of Documents in Social Media." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33460-3_14Markdown
[Mantrach and Renders. "Extension of the Rocchio Classification Method to Multi-Modal Categorization of Documents in Social Media." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/mantrach2012ecmlpkdd-extension/) doi:10.1007/978-3-642-33460-3_14BibTeX
@inproceedings{mantrach2012ecmlpkdd-extension,
title = {{Extension of the Rocchio Classification Method to Multi-Modal Categorization of Documents in Social Media}},
author = {Mantrach, Amin and Renders, Jean-Michel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {130-142},
doi = {10.1007/978-3-642-33460-3_14},
url = {https://mlanthology.org/ecmlpkdd/2012/mantrach2012ecmlpkdd-extension/}
}