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Co-clustering is based on the duality between data points (e.g. documents) and features (e.g. words), i.e. data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points. In the past decade, several co-clustering algorithms have been proposed and shown to be superior to(More)
Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization. In our model, two graphs are constructed on users and items, which exploit the internal information (e.g.(More)
Among different hybrid recommendation techniques, network-based entity recommendation methods, which utilize user or item relationship information, are beginning to attract increasing attention recently. Most of the previous studies in this category only consider a single relationship type, such as friendships in a social network. In many scenarios, the(More)
—There are many clustering tasks which are closely related in the real world, e.g. clustering the web pages of different universities. However, existing clustering approaches neglect the underlying relation and treat these clustering tasks either individually or simply together. In this paper, we will study a novel clustering paradigm, namely multi-task(More)
Depression is a common occurrence in patients with Parkinson's disease (PD), however, its pathophysiology still remains unclear. With increasing evidence suggesting that depression is a disconnection syndrome, we hypothesized that depression in PD is caused by degenerated fiber connections in the brain. We examined whole brain white matter integrity in 15(More)
Depression is a common occurrence in patients with Parkinson's disease (PD). Thus, there may be a common neural mechanism underlying the two diseases. Lewy body accumulation in specific brain areas of PD patients may damage emotion-related functions, leading to depression. Among these areas, the amygdala may present with the earliest to be damaged in PD.(More)
Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). However, in real world, there are tremendous amount of data which are distributing in a network. Existing features selection methods are not suited for networked data because the i.i.d. assumption no longer holds. This motivates us to study(More)