2 research outputs found
Scalable Learning Adaptive to Unknown Dynamics and Graphs
University of Minnesota Ph.D. dissertation.June 2019. Major: Electrical/Computer Engineering. Advisor: Georgios B. Giannakis. 1 computer file (PDF); xii, 174 pages.With the scale of information growing every day, the key challenges in machine learning include the high-dimensionality and sheer volume of feature vectors that may consist of real and categorical data, as well as the speed and the typically streaming format of data acquisition that may also entail outliers and misses. The latter may be present, either unintentionally or intentionally, in order to cope with scalability, privacy, and adversarial behavior. These challenges provide ample opportunities for algorithmic and analytical innovations in online and nonlinear subspace learning approaches. Among the available nonlinear learning tools, those based on kernels have merits that are well documented. However, most rely on a preselected kernel, whose prudent choice presumes task-specific prior information that is generally not available. It is also known that kernel-based methods do not scale well with the size or dimensionality of the data at hand. Besides data science, the urgent need for scalable tools is a core issue also in network science that has recently emerged as a means of collectively understanding the behavior of complex interconnected entities. The rich spectrum of application domains comprises communication, social, financial, gene-regulatory, brain, and power networks, to name a few. Prominent tasks in all network science applications are those of topology identification and inference of nodal processes evolving over graphs. Most contemporary graph-driven inference approaches rely on linear and static models that are simple and tractable, but also presume that the nodal processes are directly observable. To cope with these challenges, the present thesis first introduces a novel online categorical subspace learning approach to track the latent structure of categorical data `on the fly.' Leveraging the random feature approximation, it then develops an adaptive online multi-kernel learning approach (termed AdaRaker), which accounts not only for data-driven learning of the kernel combination, but also for the unknown dynamics. Performance analysis is provided in terms of both static and dynamic regrets to quantify the novel learning function approximation. In addition, the thesis introduces a kernel-based topology identification approach that can even account for nonlinear dependencies among nodes and across time. To cope with nodal processes that may not be directly observable in certain applications, tensor-based algorithms that leverage piecewise stationary statistics of nodal processes are developed, and pertinent identifiability conditions are established. To facilitate real-time operation and inference of time-varying networks, an adaptive tensor decomposition based scheme is put forth to track the topologies of time-varying networks. Last but not least, the present thesis offers a unifying framework to deal with various learning tasks over possibly dynamic networks. These tasks include dimensionality reduction, classification, and clustering. Tests on both synthetic and real datasets from the aforementioned application domains are carried out to showcase the effectiveness of the novel algorithms throughout
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A Critical Analysis of the Structural Changes Related to Craft Demographics Influencing Craft Supply and Demand in the United States Across Multiple Dimensions
The construction industry plays a major role in the United States’ economy. Currently, and during economic expansion periods, the U.S construction industry faces a workforce shortage, primarily among highly skilled trades, for two reasons: 1) strong construction demand across multiple industry sectors; and 2) low supply levels of skilled craft workers. A primary factor for the low supply of craft workers is current workers leaving the construction industry, either for other industries or retirement. The U.S. Bureau of Labor Statistics (BLS) predicted that the U.S. construction industry will be the fastest growing industry in the nation over the next decade with an estimated 1.6 million new jobs. Because of such rapid growth, 76% of construction companies in the U.S. are having difficulty finding qualified workers to fill job openings. The main objective of this dissertation was to understand construction workforce shortages and how to mitigate these shortages.The three papers (chapters from 2 to 4) contained in the body of this dissertation contribute to an understanding of the construction labor market in the U.S., focusing on the demographics that are currently influencing craft supply and demand. The first paper employed a new metric of workforce availability, using a public data set, among construction trades and regions in the U.S. Future researchers in construction or other industries can use this metric using a different sample size to similar or differing data sets. Owners and industry leaders may use this evidence in early stages of projects to mitigate the craft shortages by applying alternative management approaches. The second paper applied a longitudinal analysis of the changes in U.S. craft workers’ satisfaction and job preferences. The findings in paper 2 will guide the industry’s future recruiting and retention strategies, which should emphasize the extrinsic nature of working in the construction industry—i.e. wages. Finally, the third paper applied a comparative analysis of the utilization of multiskilling among U.S. Hispanic and non-Hispanic construction craft workers, showing skill development amongst Hispanics and non-Hispanics. Researchers and industry leaders may use this paper to help improve the career progression among Hispanic craft workers in the United States