Collaborative filtering (CF) is widely applied in recommender systems. However, the sparsity issue is still a crucial bottleneck for most existing CF methods. Although target data are extremely sparse for a newly-built CF system, some dense auxiliary data may already exist in othermatured related domains. In this paper,wepropose anovel approach, TwinBridge Transfer Learning (TBT), to address th...

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferrin...

Most language models used for natural language processing are continuous. However, the assumption of such kind of models is too simple to cope with data sparsity problem. Although many useful smoothing techniques are developed to estimate these unseen sequences, it is still important to make full use of contextual information in training data. In this paper, we propose a hierarchical word seque...

In this paper, we consider the problem of reconstructing the temporal and spatial profile of some physical phenomena monitored by large-scale Wireless Sensor Networks (WSNs) in an energy efficient manner. Compressive sensing is one of the popular choices to reduce the energy consumption of the data collection in WSNs. The existing solutions only consider sparsity of sensors' data from either te...

Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing the sampling rate in the projection view angle in computed tomography (CT). Most of the image reconstruction algorithms, developed for this purpose, solve a nonsmooth convex optimization problem involving the image total variation (TV). The TV seminorm is the ℓ1 norm of the image gradient magnit...

Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to c...