Statistical word alignment often suffers from data sparseness. Part-of-speeches are often incorporated in NLP tasks to reduce data sparseness. In this paper, we attempt to mitigate such problem by reflecting alignment tendency between part-of-speeches to statistical word alignment. Because our approach does not rely on any language-dependent knowledge, it is very simple and purely statistic to ...

Most of the image compression techniques currently available were designed mainly with the aim of compressing continuous-tone natural images. However, if this assumption is not verified, such as in the case of histogram sparseness, a degradation in compression performance may occur. In this paper, we analyze the impact of histogram sparseness in three state-of-the-art lossless image compression...

1. To analyze the selectivity and the sparseness of firing to visual stimuli of single neurons in the primate temporal cortical visual area, neuronal responses were measured to a set of 68 visual stimuli in macaques performing a visual fixation task. The population of neurons analyzed had responses that occurred primarily to faces. The stimuli included 23 faces, and 45 nonface images of real-wo...

Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L1- and L∞-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed pena...

Recommender systems using traditional collaborative filtering suffer from some significant weaknesses, such as data sparseness and scalability. In this study, we propose a method that can improve the recommender systems by combining similarity, trust and reputation. We modify the way that neighbors are selected by introducing the trust and reputation metrics in order to develop new relations be...

The use of exemplar-based techniques for both speech classification and recognition tasks has become increasingly popular in recent years. However, the notion of why sparseness is important for exemplar-based speech processing has been relatively unexplored. In addition, little analysis has been done in speech processing on the appropriateness of different types of sparsity regularization const...

This paper addresses the problem of noise power spectrum estimation. Existing noise estimation methods cannot perform quite reliably when noise level increasing abruptly (e.g., narrowband noise bursts). To overcome this problem, we improve the time-recursive averaging algorithm based on speech presence probability (SPP), by exploiting the sparseness of speech spectrum. Firstly, we utilize the S...

Considering the sparseness property of images, a sparse representation based iterative deblurring method is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken is based on sparse and redundant representations over adaptively training dictionaries from single blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary th...