کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4948356 1439611 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum entropy discrimination factor analyzers
ترجمه فارسی عنوان
آنالایزر عامل فاکتور تبخیر حداکثر آنتروپی
کلمات کلیدی
مدل سازی بزرگ حاشیه، تبعیض حداکثر آنتروپی، استنتاج میانه میدان، نمایندگی متغیر وابسته، تجزیه کننده فاکتور،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Devising generative models that allow for inferring low dimensional latent feature representations of high-dimensional observations is a significant problem in statistical machine learning. Factor analysis (FA) is a well-established linear latent variable scheme addressing this problem by modeling the covariances between the elements of multivariate observations under a set of linear assumptions. FA is closely related to principal components analysis (PCA), and might be considered as a generalization of both PCA and its probabilistic version, PPCA. Recently, the invention of Gaussian process latent variable models (GP-LVMs) has given rise to a whole new family of latent variable modeling schemes that generalize FA under a nonparametric Bayesian inference framework. In this work, we examine generalization of FA models under a different Bayesian inference perspective. Specifically, we propose a large-margin formulation of FA under the maximum entropy discrimination (MED) framework. The MED framework integrates the large-margin principle with Bayesian posterior inference in an elegant and computationally efficient fashion, allowing to leverage existing high-performance solvers for convex optimization problems. We devise efficient mean-field inference algorithms for our model, and exhibit its advantages by evaluating it in a number of diverse application scenarios, dealing with high-dimensional data classification and reconstruction.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 216, 5 December 2016, Pages 409-415
نویسندگان
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