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Published byAnastasia Banbury Modified over 4 years ago

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January 23 rd, 2011 1

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Document classification task We are interested to solve a task of Text Classification, i.e. to automatically assign a given document to a fixed number of semantic categories. In general, text classification is a multi-class problem, where each document may belong to one, many, or none of the categories. Many algorithms were proposed to handle this task, part of them are trained using semi-supervised text. 2

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Semi-supervised learning (SSL) Semi-supervised learning (SSL) employs small amount of labeled data with relatively large amount of unlabeled data to train classifiers. Graph-based SSL algorithms are an important class of SSL techniques, where the data (both labeled and unlabeled) is embedded within a low-dimensional manifold expressed by a graph. 3

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New approach for graph-based semi-supervised text classification Previous work on semi-supervised text classification has relied primarily on one vs. rest approaches to overcome the multi-class nature of the text classification problem. Such an approach may be sub-optimal since it disregards data overlaps. In order to address this drawback, a new framework is proposed based on optimizing a loss function composed of Kullback-Leiber divergence between probability distribution defined for each graph vertex. The use of probability distributions, rather than fixed integer labels, leads to straightforward multi-class generalization. 4

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Learning problem definition 5

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Multi-class SSL optimization procedure Lets define two sets of probability distribution over the elements of : where the later is fixed throughout the training. we set to be uniform over only the possible categories and zero otherwise: where is a set of possible outputs for input. The goal of the learning is to find the best set of distributions by minimizing the next objective function: where denotes the entire set of distributions to be learned, is the standard entropy function of, is the KL-divergence between and, and are positive parameters. The final labels for are then given by 6

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Analysis for the objective function 7

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Learning with alternating minimization 8

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The update equations 11

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Conclusions 12 The paper introduce a new graph based semi supervised learning approach for document classification. This approach uses Kullback-Leiber divergence between probability distributions in the objective function instead of Euclidian distances, thus allowing for each document to be a member of different classes. Unfortunately the objective derivative didnt yield closed form solution, so an alternating minimization approach was proposed. This approach admits a closed form and relatively simple solution for each iteration.

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Any questions? 13

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