Probabilistic Principal Component Analysis using Expectation Maximization (PPCA-EM) for Analyzing 3D Volumes with Missing Data
نویسندگان
چکیده
منابع مشابه
Modelling Handwitten Digit Data using Probabilistic Principal Component Analysis
Principal Component Analysis (PCA) is one century old now. Nevertheless, it still undergoes research and new extensions are found. Probabilistic Principal Component Analysis (PPCA, proposed by Tipping and Bishop) is one of these recent PCA extensions. PPCA defines a probabilistic generative model for PCA.It can easily be extended to mixture models. Among recent mixture density theoretical devel...
متن کاملThe Expectation Maximization (EM) algorithm
In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly incl...
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ژورنال
عنوان ژورنال: Microscopy and Microanalysis
سال: 2010
ISSN: 1431-9276,1435-8115
DOI: 10.1017/s143192761005734x