6,032 research outputs found
Learning Bayesian Networks with Incomplete Data by Augmentation
We present new algorithms for learning Bayesian networks from data with
missing values using a data augmentation approach. An exact Bayesian network
learning algorithm is obtained by recasting the problem into a standard
Bayesian network learning problem without missing data. To the best of our
knowledge, this is the first exact algorithm for this problem. As expected, the
exact algorithm does not scale to large domains. We build on the exact method
to create an approximate algorithm using a hill-climbing technique. This
algorithm scales to large domains so long as a suitable standard structure
learning method for complete data is available. We perform a wide range of
experiments to demonstrate the benefits of learning Bayesian networks with such
new approach
LEARNING BAYESIAN NETWORKS FOR REGRESSION FROM INCOMPLETE DATABASES*
In this paper we address the problem of inducing Bayesian network models for regression from incomplete databases. We use mixtures of truncated exponentials (MTEs) to represent the joint distribution in the induced networks. We consider two particular Bayesian network structures, the so-called na¨ıve Bayes and TAN, which have been successfully used as regression models when learning from complete data. We propose an iterative procedure for inducing the models, based on a variation of the data augmentation method in which the missing values of the explanatory variables are filled by simulating from their posterior distributions, while the missing values of the response variable are generated using the conditional expectation of the response given the explanatory variables. We also consider the refinement of the regression models by using variable selection and bias reduction. We illustrate through a set of experiments with various databases the performance of the proposed algorithms
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
Quality assessment of medical images is essential for complete automation of
image processing pipelines. For large population studies such as the UK
Biobank, artefacts such as those caused by heart motion are problematic and
manual identification is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) images. As this is a highly
imbalanced classification problem (due to the high number of good quality
images compared to the low number of images with motion artefacts), we propose
a novel k-space based training data augmentation approach in order to address
this problem. Our method is based on 3D spatio-temporal Convolutional Neural
Networks, and is able to detect 2D+time short axis images with motion artefacts
in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset
consisting of 3465 CMR images and achieve not only high accuracy in detection
of motion artefacts, but also high precision and recall. We compare our
approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Product reviews and ratings on e-commerce websites provide customers with
detailed insights about various aspects of the product such as quality,
usefulness, etc. Since they influence customers' buying decisions, product
reviews have become a fertile ground for abuse by sellers (colluding with
reviewers) to promote their own products or to tarnish the reputation of
competitor's products. In this paper, our focus is on detecting such abusive
entities (both sellers and reviewers) by applying tensor decomposition on the
product reviews data. While tensor decomposition is mostly unsupervised, we
formulate our problem as a semi-supervised binary multi-target tensor
decomposition, to take advantage of currently known abusive entities. We
empirically show that our multi-target semi-supervised model achieves higher
precision and recall in detecting abusive entities as compared to unsupervised
techniques. Finally, we show that our proposed stochastic partial natural
gradient inference for our model empirically achieves faster convergence than
stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, 2019. Contains supplementary material. arXiv admin note: text
overlap with arXiv:1804.0383
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