Fast dictionary learning from incomplete data

Fast dictionary learning from incomplete data

This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low-rank component in the data and provides a strategy for recovering this low-rank component again from incomplete data. Several synthetic e...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Learning from Incomplete Data

Survey non-response is an important problem in statistics, economics and social sciences. The paper reviews the missing data framework of Little & Rubin [Little and Rubin, 1986]. It presents a survey of techniques to deal with non-response in surveys using a likelihood based approach. The focuses on the case where the probability of a data missing depends on its value. The paper uses the two-st...

Learning from Incomplete Data

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives|the likelihood-based and the Bayesian. The goal is twofold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorith...