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Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coefficients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly(More)
—Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In this paper, we propose Non-negative Multiple Tensor(More)
—The amount and variety of multimedia data such as images, movies and music available on over social networks are increasing rapidly. However, the ability to analyze and exploit these unorganized multimedia data remains inadequate, even with state-of-the-art media processing techniques. Our finding in this paper is that the emerging social curation service(More)
We introduce the localized Lasso, which is suited for learning models that both are interpretable and have a high predictive power in problems with high dimensionality d and small sample size n. More specifically, we consider a function defined by local sparse models, one at each data point. We introduce sample-wise network regularization to borrow strength(More)
We propose a cross-domain recommendation method for predicting the ratings of items in different domains, where neither users nor items are shared across domains. The proposed method is based on matrix factoriza-tion, which learns a latent vector for each user and each item. Matrix factorization techniques for a single-domain fail in the cross-domain(More)
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