Heterogeneous Transfer Learning for Image Clustering via the SocialWeb

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 1–9, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Heterogeneous Transfer Learning for Image Clustering via the Social Web Qiang Yang Hong Kong University of Science and Technology, Clearway Bay, Kowloon, Hong Kong qyang@cs.ust.hk Yuqiang Chen Gui-Rong Xue Wenyuan Dai Yong Yu Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China {yuqiangchen,grxue,dwyak,yyu}@apex.sjtu.edu.cn Abstract In this paper, we present a new learning scenario, heterogeneous transfer learn- ing, which improves learning performance when the data can be in different feature spaces and where no correspondence be- tween data instances in these spaces is pro- vided. In the past, we have classified Chi- nese text documents using English train- ing data under the heterogeneous trans- fer learning framework. In this paper, we present image clustering as an exam- ple to illustrate how unsupervised learning can be improved by transferring knowl- edge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense dis- ambiguation in query-based image search, but its quality is often low due to image- data sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature repre- sentations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous trans- fer learning based on the auxiliary data is indeed effective and promising. 1 Introduction Traditional machine learning relies on the avail- ability of a large amount of data to train a model, which is then applied to test data in the same feature space. However, labeled data are often scarce and expensive to obtain. Various machine learning strategies have been proposed to address this problem, including semi-supervised learning (Zhu, 2007), domain adaptation (Wu and Diet- terich, 2004; Blitzer et al., 2006; Blitzer et al., 2007; Arnold et al., 2007; Chan and Ng, 2007; Daume, 2007; Jiang and Zhai, 2007; Reichart and Rappoport, 2007; Andreevskaia and Bergler, 2008), multi-task learning (Caruana, 1997; Re- ichart et al., 2008; Arnold et al., 2008), self-taught learning (Raina et al., 2007), etc. A commonality among these methods is that they all require the training data and test data to be in the same fea- ture space. In addition, most of them are designed for supervised learning. However, in practice, we often face the problem where the labeled data are scarce in their own feature space, whereas there may be a large amount of labeled heterogeneous data in another feature space. In such situations, it would be desirable to transfer the knowledge from heterogeneous data to domains where we have rel- atively little training data available. To learn from heterogeneous data, researchers have previously proposed multi-view learning (Blum and Mitchell, 1998; Nigam and Ghani, 2000) in which each instance has multiple views in different feature spaces. Different from previous works, we focus on the problem of heterogeneous transfer learning, which is designed for situation when the training data are in one feature …

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