$e$PCA: High dimensional exponential family PCA
نویسندگان
چکیده
منابع مشابه
ePCA: High Dimensional Exponential Family PCA
Many applications involve large collections of high-dimensional datapoints with noisy entries from exponential family distributions. It is of interest to estimate the covariance and principal components of the noiseless distribution. In photon-limited imaging (e.g. XFEL) we want to estimate the covariance of the pixel intensities of 2-D images, where the pixels are low-intensity Poisson variabl...
متن کاملBayesian Exponential Family PCA
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential family PCA and non-negative matrix factorisation have successfully extended PCA to non-Gaussian data types, but these techniques fail to take advantage of Bayesian inference and can suffer from problems of overfitting ...
متن کاملSemi-parametric Exponential Family PCA
We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimensional, multimodal distribution. In addition, we allow the components of latent variable models to be drawn from the...
متن کاملInference for High-dimensional Exponential Family Graphical Models
Probabilistic graphical models have been widely used to model complex systems and aid scientific discoveries. Most existing work on highdimensional estimation of exponential family graphical models, including Gaussian and Ising models, is focused on consistent model selection. However, these results do not characterize uncertainty in the estimated structure and are of limited value to scientist...
متن کاملExponential Family PCA for Belief Compression in POMDPs
Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are intractable for large models. The intractability of these algorithms is due to a great extent to their generating an optimal policy over the entire belief space. However, in real POMDP problems most belief states are unlikely, and there is a structured, low-dimensional manifold...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2018
ISSN: 1932-6157
DOI: 10.1214/18-aoas1146