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ContributorsASU Library. Music Library (Publisher)
Created2018-04-09
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Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering

Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsJin, Leon (Performer) / Duo, Hongzuo (Performer) / Bergstedt, David (Performer) / Ellis, Gage (Performer) / Novak, Gail (Performer) / ASU Library. Music Library (Publisher)
Created2021-02-24
ContributorsASU Library. Music Library (Publisher)
Created2021-02-22
ContributorsWaters, Jared (Performer) / Creviston, Hannah (Performer) / Liu, Miao (Performer) / Guo, Hongzuo (Performer) / DeLaCruz, Nathaniel (Performer) / LoGuidice, Rosa (Performer) / Chiko, Ty (Performer) / Gatchel, David (Performer) / ASU Library. Music Library (Publisher)
Created2021-01-28
ContributorsKosminov, Vladislav (Performer) / Solari, John (Performer) / Liu, Shiyu (Performer) / Huang, Anruo (Performer) / Holly, Sean (Performer) / Novak, Gail (Performer) / Yang, Elliot (Performer) / Wu, Selene (Performer) / Kinnard, Zachariah (Performer) / Kuebelbeck, Stephen (Performer) / Johnson, Kaitlyn (Performer) / Bosworth, Robert (Performer) / Matejek, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2021-01-27
ContributorsASU Library. Music Library (Publisher)
Created2021-04-22
ContributorsSuehiro, Dylan (Conductor) / Kelley, Karen (Performer) / Ladley, Teddy (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-19
ContributorsASU Library. Music Library (Publisher)
Created2021-04-12
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Description
In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many

In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many community-based chamber music ensembles have been formed throughout the United States. These groups not only focus on performing classical music, but serve the needs of their communities as well. The problem, however, is that many musicians have not learned the business skills necessary to create these career opportunities. In this document I discuss the steps ensembles must take to develop sustainable careers. I first analyze how groups build a strong foundation through getting to know their communities and creating core values. I then discuss branding and marketing so ensembles can develop a public image and learn how to publicize themselves. This is followed by an investigation of how ensembles make and organize their money. I then examine the ways groups ensure long-lasting relationships with their communities and within the ensemble. I end by presenting three case studies of professional ensembles to show how groups create and maintain successful careers. Ensembles must develop entrepreneurship skills in addition to cultivating their artistry. These business concepts are crucial to the longevity of chamber groups. Through interviews of successful ensemble members and my own personal experiences in the Tetra String Quartet, I provide a guide for musicians to use when creating a community-based ensemble.
ContributorsDalbey, Jenna (Author) / Landschoot, Thomas (Thesis advisor) / McLin, Katherine (Committee member) / Ryan, Russell (Committee member) / Solis, Theodore (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2013