There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Nonnegative Matrix Factorization (NMF) can be good solution for the search results clustering. 1
Determination of the appropriate number of clusters is a big challenge for the bi-clustering method ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning becau...
This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
Abstract—Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce th...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Vari...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating ...
Abstract. Given a nonnegative matrix M, the orthogonal nonnegative matrix factorization (ONMF) probl...
Abstract Nonnegative matrix factorization (NMF) provides a lower rank approx-imation of a matrix by ...
Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative...
In this article we propose a method to refine the clustering results obtained with the nonnegative m...
In this article we propose a method to refine the clustering results obtained with the nonnegative m...
Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning ...
Determination of the appropriate number of clusters is a big challenge for the bi-clustering method ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning becau...
This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
Abstract—Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce th...
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Vari...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating ...
Abstract. Given a nonnegative matrix M, the orthogonal nonnegative matrix factorization (ONMF) probl...
Abstract Nonnegative matrix factorization (NMF) provides a lower rank approx-imation of a matrix by ...
Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative...
In this article we propose a method to refine the clustering results obtained with the nonnegative m...
In this article we propose a method to refine the clustering results obtained with the nonnegative m...
Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning ...
Determination of the appropriate number of clusters is a big challenge for the bi-clustering method ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning becau...