Simplex Volume Maximization (SiVM) exploits distance geometry for efficiently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Matrix factorization methods are among the most common techniques for detecting latent components in...
© 1989-2012 IEEE. Matrix factorization has been widely applied to various applications. With the fas...
Climate change, the global energy footprint, and strategies for sustainable development have become ...
Low rank matrix factorization is an important step in many high dimensional machine learning algorit...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
145 pagesPropelled by large datasets and parallel compute accelerators, deep neural networks have re...
The Maximum Inner Product Search (MIPS) problem, prevalent in matrix factorization-based recommender...
The novel algorithm proposed in this thesis will improve the non-negative matrix factorization. It w...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Matrix factorization (or low-rank matrix completion) with missing data is a key computation in many ...
The metric search paradigm has been to this day successfully applied to several real-world problems,...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Matrix factorization methods are among the most common techniques for detecting latent components in...
© 1989-2012 IEEE. Matrix factorization has been widely applied to various applications. With the fas...
Climate change, the global energy footprint, and strategies for sustainable development have become ...
Low rank matrix factorization is an important step in many high dimensional machine learning algorit...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
145 pagesPropelled by large datasets and parallel compute accelerators, deep neural networks have re...
The Maximum Inner Product Search (MIPS) problem, prevalent in matrix factorization-based recommender...
The novel algorithm proposed in this thesis will improve the non-negative matrix factorization. It w...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Matrix factorization (or low-rank matrix completion) with missing data is a key computation in many ...
The metric search paradigm has been to this day successfully applied to several real-world problems,...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
In this paper, we propose a model for representing and predicting distances in large-scale networks ...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
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