We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can onl...

This report introduces a novel sparse decomposition model for hyperspectral image reconstruction. The model integrates two well-known sparse structures of hyperspectral images: a small set of signature spectral vectors span all spectral vectors (one at each pixel), and like a standard image, a hyperspectral image is spatially redundant. In our model, a threedimensional hyperspectral cube X is f...

Pair-wise ranking methods have been widely used in recommender systems to deal with implicit feedback. They attempt to discriminate between a handful of observed items and the large set of unobserved items. In these approaches, however, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this paper, we...

SMT approaches face the problem of data sparsity while translating into a morphologically rich language. It is very unlikely for a parallel corpus to contain all morphological forms of words. We propose a solution to generate these unseen morphological forms and inject them into original training corpora. We observe that morphology injection improves the quality of translation in terms of both ...

Data-driven Machine Translation (MT) systems have been found to require large amounts of data to function well. However, obtaining parallel texts for many languages is time-consuming, expensive and difficult. This thesis aims at improving translation quality for languages that have limited resources by making use of the available data more efficiently. Templates or generalizations of sentence-p...

In this paper we consider the problem of joint enhancement of multichannel Synthetic Aperture Radar (SAR) data. Previous work by Cetin and Karl introduced nonquadratic regularization methods for image enhancement using sparsity enforcing penalty terms. For multichannel data, independent enhancement of each channel is shown to degrade the relative phase information across channels that is useful...

The hierarchical non-negative matrix factorization (HNMF) is a multilayer generative network for decomposing strictly positive data into strictly positive activations and base vectors in a hierarchical manner. However, the standard hierarchical NMF is not suited for overcomplete representations and does not code efficiently for transformations in the input data. Therefore we extend the standard...

The concept classifier has been used as a translation unit in speech-to-speech translation systems. However, the sparsity of the training data is the bottle neck of its effectiveness. Here, a new method based on using a statistical machine translation system has been introduced to mitigate the effects of data sparsity for training classifiers. Also, the effects of the background model which is ...

A common problem with OnLine Analytical Processing (OLAP) databases is data explosion data size multiplies, when it is loaded from the source data into multidimensional cubes. Data explosion is not an issue for small databases, but can be serious problems with large databases. In this paper we discuss the sparsity and data explosion phenomenon in multidimensional data model, which lie at the co...

Morphologically rich languages generally require large amounts of parallel data to adequately estimate parameters in a statistical Machine Translation(SMT) system. However, it is time consuming and expensive to create large collections of parallel data. In this paper, we explore two strategies for circumventing sparsity caused by lack of large parallel corpora. First, we explore the use of dist...