document: Co-clustering based classification for out-of-domain documents In many real world applications , labeled data are in short supply . It often happens that obtaining labeled data in a new domain is expensive and time consuming , while there may be plenty of labeled data from a related but different domain . Traditional machine learning is not able to cope well with learning across different domains . In this paper , we address this problem for a text-mining task , where the labeled data are under one distribution in one domain known as in-domain data , while the unlabeled data are under a related but different domain known as out-of-domain data . Our general goal is to learn from the in-domain and apply the learned knowledge to out-of-domain . We propose a co-clustering based classification ( CoCC ) algorithm to tackle this problem . Co-clustering is used as a bridge to propagate the class structure and knowledge from the in-domain to the out-of-domain . We present theoretical and empirical analysis to show that our algorithm is able to produce high quality classification results , even when the distributions between the two data are different . The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms .
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