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 sparse data, because the information inc...

the realization optimality theory is a recent development in the original optimality theory which is proposed to deal with morphological issues especially the inflectional ones. its main idea is to consider the morphological realization rules as ranked violable language-specific constraints that control the realization processes and provide phonological information of grammatical morphemes. thi...

Short text clustering has become an increasing important task with the popularity of social media, and it is a challenging problem due to its sparseness of text representation. In this paper, we propose a Short Text Clustering via Convolutional neural networks (abbr. to STCC), which is more beneficial for clustering by considering one constraint on learned features through a self-taught learnin...

Reconstructing biomolecular networks from time series mRNA or protein abundance measurements is a central challenge in computational systems biology. The regulatory processes behind cellular responses are coupled and nonlinear, leading to rich dynamical behavior. One class of reconstruction algorithms uses regression and penalized regression to impose sparseness on the solution, as requested bi...

Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combine...

Statistical language models are useful because they can provide probabilistic information upon uncertain decision making. The most common statistic is n-grams measuring word cooccurrences in texts. The method suffers from data shortage problem, however. In this paper, we suggest Bayesian networks be used in approximating the statistics of insufficient occurrences and of those that do not occur ...

In 1973 Paul Erdős conjectured that there is an integer v0(r) such that, for every v > v0(r) and v ≡ 1,3 (mod 6), there exists a Steiner triple system of order v, containing no i blocks on i + 2 points for every 1 < i ≤ r . Such an STS is said to be r-sparse. In this paper we consider relations of automorphisms of an STS to its sparseness. We show that for every r ≥ 13 there exists no point-tra...