A new discriminative RBM for supervised learning known as Classification RBM (ClassRBM) was proposed in 2008. The Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs) methods have been successfully applied for unsupervised learning. The method is the extension to the Classification Restricted Boltzmann Machine (ClassRBM). In the last topic, we propose a novel deep learning approach for classification of multimedia data. We find that the proposed methodology would have raised alarms for regulators prior to several key events and announcements by the European Central Bank during the 2007-2009 financial crisis, demonstrating the promise of the approach as an early warning system. Afterwards, Exponential Weighted Moving Average (EWMA) control charts are used to monitor the network sequence in real time in order to distinguish the gradual change resulting from the typical edge dynamics from abrupt changes in trading patterns caused by fundamental changes in market conditions. The approach combines a state space model with the Hurdle model to capture temporal dynamics of the edge formation process, which is modeled as a function of node and edge attributes and estimated using an extended Kalman Filter. Hence, we create a monitoring system to detect changes within a sequence of sparse networks constructed from an interbank lending market in the European Union. As such, network analysis has become a critical tool for assessing interconnectedness and systemic risk levels. The interconnectedness of financial institutions can function as a mechanism for the propagation and amplification of shocks throughout the economy, thus contributing to financial crises. For this, we focus on modeling the network connections in financial institutions. For the second topic, we propose a novel methodology for dynamically monitoring sparse networks. We theoretically motivate our approach and do performance evaluation of our integrated Monitoring and Diagnostics method through simulation and case studies. The PC-based Signal Recovery (PCSR) diagnostics approach draws inspiration from Compressed Sensing to use Adaptive Lasso for identifying the sparse change in the process. More importantly, we integrate a novel diagnostic approach to enable a streamlined SPC. Consequently, we develop a novel monitoring method based on this principle named Adaptive PC Selection (APC). Therefore, we show that adaptively chosen PCs are significantly better for process monitoring. However, we argue that this is an inappropriate approach for the purpose of monitoring. For PCA-based monitoring, most of the existing methods focus on PCs with the highest variance. For monitoring, one commonly used method in high dimensions are based on Principal Component Analysis (PCA). As a first topic, we propose a new monitoring and diagnosis approach based on PCA for monitoring high-dimensional, multi-stream data. Of the plethora of problems that exist, this dissertation attempts to focus on three of them. Based on our results, the PCA-LASSO method shows promise in identifying gene-gene interactions, and, at this time we suggest using it with other conventional approaches, such as generalized linear models, to narrow down genetic signals.This dissertation concentrates on solving problems related to monitoring and predicting high-dimensional, streaming data using new data mining methods. We demonstrated these methods with the Genetic Analysis Workshop 16 rheumatoid arthritis genome-wide association study data and our results identified a few gene-gene signals. This method was compared to placing the raw SNP values into the LASSO and the logistic model with individual gene-gene interaction. We have extended the PCA-LASSO approach using the bootstrap to estimate the standard errors and confidence intervals of the LASSO coefficient estimates. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects.
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