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As the emergement of high-throughput measurement technologies, we are entering the big data era. Modern data are often generated from heterogeneous multiple sources, thus can be called multi-view data. The challenge of effectively integrating such data for decision making and novel knowledge discovery is raised. Matrix factorization methods have historically played important roles in various analyses...
Distance metric learning (DML) is an effective similarity learning tool to learn a distance function from examples to enhance the model performance in applications of classification, regression, and ranking, etc. Most DML algorithms need to learn a Mahalanobis matrix, a positive semidefinite matrix that scales quadratically with the number of dimensions of input data. This brings huge computational...
Recently in [1], [2], a new kernel Fisher type contrast measure has been proposed to extract a target population in a data set contaminated by outliers. Although mathematically sound, this work presents some further shortcomings. First, the performance of the method relies on the assumption that the density between the target data and outliers is different. However, this consideration can easily prove...
We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint. We propose a Bayesian model for linear regression in crowdsourcing and use variational inference for parameter estimation. To minimize the number of labels crowdsourced from the annotators, we adopt an active learning approach. In this specific context,...
Saliency computational model with active environment perception can substantially facilitate a wide range of applications. Conventional saliency computational models primarily rely on hand-crafted low level image features, such as color or contrast. However, they may face great challenges in low lighting scenario, due to the lack of well-defined feature to represent saliency information in low contrast...
In this paper, an information-theoretic-based adaptive resonance theory (IT-ART) neural network architecture is presented. Each IT-ART category is defined by the first and second order statistics (mean and covariance matrix) of the cluster or class it represents. This information is used to estimate probability density functions (multivariate Gaussians) and compute the activation functions. The match...
An important feature present in neural network models is their ability to learn from data, even when the user does not have much information about the particular dataset. However, the most popular models do not perform well in spatial interpolation problems due to their difficulty in accurately modeling spatial correlation between samples. On the other hand, one of the most important geostatistical...
As a nonlinear extension of Kalman filter, the extended Kalman filter (EKF) is also based on the minimum mean square error (MMSE) criterion. In general, the EKF performs well in Gaussian noises. But its performance may deteriorate substantially when the system is disturbed by heavy-tailed impulsive noises. In order to improve the robustness of EKF against impulsive noises, a new filter for nonlinear...
We address the problem of estimating high-dimensional covariance matrices (CM) for the explicit purpose of supervised anomaly detection, in the case when the number n of data points is lower than their dimensionality p. This is increasingly common with the emergence of the Internet of Things that makes it possible to collect data from many sensors simultaneously, resulting in very high-dimensional...
This paper presents a discrete-time sliding mode control design based on a neural model for induction motors. A Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) is used to identify the model. The sliding mode controller is designed to force the system to track a torque reference and a flux magnitude. Then, a reduced order observer is designed for rotor fluxes...
The Gaussian process latent variable model (GPLVM) had been proved to be good at discovering low-dimension manifold from nonlinear high-dimensional data for small training sets. However, for labeled data, GPLVM cannot achieve a better result because it doesn't use the label information. It turned out to be an effective strategy to employ a discriminative prior over the latent space according to the...
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