Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Regularized Optimal Transport and the Rot Mover's Distance

This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems. In this context, the regularized optimal transport turns out to be equivalent to a matrix nearness problem with respect to Bregman divergences. Our framework thus naturally generalizes a previously proposed regularization based on the Boltzmann-Shannon entropy related to the Kullback-Leibler divergence, and solved with the Sinkhorn-Knopp algorithm. We call the regularized optimal transport distance the rot mover's distance in reference to the classical earth mover's distance. We develop two generic schemes that we respectively call the alternate scaling algorithm and the non-negative alternate scaling algorithm, to compute efficiently the regularized optimal plans depending on whether the domain of the regularizer lies within the non-negative orthant or not. These schemes are based on Dykstra's algorithm with alternate Bregman projections, and further exploit the Newton-Raphson method when applied to separable divergences. We enhance the separable case with a sparse extension to deal with high data dimensions. We also instantiate our proposed framework and discuss the inherent specificities for well-known regularizers and statistical divergences in the machine learning and information geometry communities. Finally, we demonstrate the merits of our methods with experiments using synthetic data to illustrate the effect of different regularizers and penalties on the solutions, as well as real-world data for a pattern recognition application to audio scene classification

Machine Learning Aided Production Data Analysis for Estimated Ultimate Recovery Forecasting

Estimated ultimate recovery (EUR) predictions are important in the petroleum industry. Many researchers have worked on implementing accurate EUR predictions. In this study, we used machine learning techniques to help predict the EUR range. We analyzed 200 Barnett shale wells with less than 170 months production history. We forecasted the production profile for each well using the modified Arps hyperbolic decline model. With the EUR values for 200 wells available, we forecasted the EUR of wells with limited production history by using three machine learning techniques, neural networks (NNet), support vector machine (SVM) and random forest (RF). The results show that the 200 sorted EUR values predicted with the commercial decline analysis software, ValNav, follows a lognormal distribution as indicated on a log-probability paper plot. The P90, P50 and P10 EUR values were identified and the low P10/P90 value of 2.3 shows a low variance of EUR in this geologic area. The production data were separated into eight groups and processed before being fed into the 3 machine learning algorithms. A four-fold cross-validation technique was employed to reduce the generalization error of the trained classifiers. The details of these 3 algorithms were also introduced. NNet performed best with highest test accuracy of 0.97 among the three machine learning algorithms employed with wells of 170 months’ production history. In addition, we also tested the EUR prediction performance with 24, 48, 96, and 170 months’ production history. The result shows that when we predict the wells’ EUR with increasing production history, we could achieve more accurate forecasting performance. The results in this project can be used to help oil and gas companies make financial decisions based on available production data in the same geologic area. Also, this project can also help provide a basis for researchers who are interested in this direction. Robustness analysis was implemented. The robustness of the algorithm is defined as the total distance of misclassified types to the correct types. Less total distance corresponds to more reliable and more stable performance for each individual algorithm. The NNet gives more robust performance with 100% misclassified samples classified into the types within one type distance to the correct types. RF is least robust. As the production history increases, the robustness of the three algorithms increases

Positive Semidefinite Metric Learning Using Boosting-like Algorithms

The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.Comment: 30 pages, appearing in Journal of Machine Learning Researc