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In this paper, we address the subspace clustering problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into their respective subspaces and remove possible outliers as well. To this end, we propose a novel objective function named Low-Rank Representation (LRR), which seeks(More)
This paper explores the use of social annotations to improve websearch. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite webpages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of(More)
Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these(More)
Providing a natural language interface to ontologies will not only offer ordinary users the convenience of acquiring needed information from ontologies, but also expand the influence of ontologies and the semantic web consequently. This paper presents PANTO, a Portable nAtural laNguage inTerface to Ontologies, which accepts generic natural language queries(More)
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(More)
This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data,(More)
In order to obtain a machine understandable semantics for web resources, research on the Semantic Web tries to annotate web resources with concepts and relations from explicitly defined formal ontologies. This kind of formal annotation is usually done manually or semi-automatically. In this paper, we explore a complement approach that focuses on the "social(More)