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| RESCAL factorization (taken from [Nickel, 2013]) |
If you are interested in details of the problem, this software aims to solve, see the previous post on Machine Learning with Knowledge Graphs.
Личный блог о поездках (Россия, Крит, Норвегия, США, Атланта, Сан-Франциско, Кремниевая долина, Австралия), науке и технологиях (machine learning, computer science, data mining, information retrieval, семантический поиск, Semantic Web, программа Фулбрайта, Fulbright, Казанский федеральный университет), программировании (Java, Scala, R, Python), хобби (Quora, музыка, концерты, бренды часов, игры, автомобили, телевидение) и политике (Навальный, Путин, Медведев, Саакашвили, Ли Куан Ю, Брежнев).
суббота, 11 октября 2014 г.
Scalable tensor factorization: Ext-RESCAL 0.7 is out
I've published Ext-RESCAL 0.7 on GitHub. The version has critical fixes in using proper sparse matrix types (SciPy module) for representing tensor slices. In my current experiments, I could handle a tensor (derived from DBpedia 3.9) of ~ 6 million x 6 million x 1000 dimensions, including over 180 million non-zero values, on ~ 70GB RAM. To the best of my knowledge, this is the largest dataset, for which RESCAL has been used so far.
If you are interested in details of the problem, this software aims to solve, see the previous post on Machine Learning with Knowledge Graphs.
If you are interested in details of the problem, this software aims to solve, see the previous post on Machine Learning with Knowledge Graphs.
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