Open AccessScalable and Interpretable Data Representation for High-Dimensional, Complex Data
Author(s) -
Been Kim,
Kayur Patel,
Afshin Rostamizadeh,
Julie Shah
Publication year - 2015
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v29i1.9474
Subject(s) - interpretability , computer science , representation (politics) , scalability , inference , latent dirichlet allocation , external data representation , machine learning , artificial intelligence , data mining , feature (linguistics) , enhanced data rates for gsm evolution , feature learning , data point , topic model , linguistics , philosophy , database , politics , political science , law
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