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You searched for +publisher:"University of Houston" +contributor:("Burks, Jared"). Showing records 1 – 2 of 2 total matches.

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University of Houston

1. Xu, Yan. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.

Degree: Electrical and Computer Engineering, Department of, 2015, University of Houston

The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit of prior information. A subspace trend is a sustained pattern of gradual/progressive changes within an unknown subset of feature dimensions. A fundamental challenge to subspace trend discovery is the presence of irrelevant data dimensions, noise, outliers, and confusion from multiple subspace trends driven by independent factors that are mixed in with each other. These factors can obscure the trends in traditional dimension reduction and projection based data visualizations. To overcome these limitations, we propose a novel graph-theoretic neighborhood similarity measure for sensing concordant progressive changes across data dimensions. Using this measure, we present an unsupervised algorithm for trend-relevant feature selection and visualization. Additionally, we propose to use an efficient online density-based representation to make the algorithm scalable for massive datasets. The representation not only assists in trend discovery, but also in cluster detection including rare populations. Our method has been successfully applied to diverse synthetic and real-world biomedical datasets, such as gene expression microarray and arbor morphology of neurons and microglia in brain tissue. Derived representations revealed biologically meaningful hidden subspace trend(s) that were obscured by irrelevant features and noise. Although our applications are mostly from the biomedical domain, the proposed algorithm is broadly applicable to exploratory analysis of high-dimensional data including visualization, hypothesis generation, knowledge discovery, and prediction in diverse other applications. Advisors/Committee Members: Roysam, Badrinath (advisor), Mayerich, David (committee member), Burks, Jared (committee member), Qiu, Peng (committee member), Prasad, Saurabh (committee member), Han, Zhu (committee member).

Subjects/Keywords: trend; visualization; biomedical; unsupervised learning; feature selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xu, Y. (2015). Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3672

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Thesis, University of Houston. Accessed June 16, 2019. http://hdl.handle.net/10657/3672.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Web. 16 Jun 2019.

Vancouver:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Internet] [Thesis]. University of Houston; 2015. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10657/3672.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Thesis]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/3672

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Houston

2. -7613-6444. Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets.

Degree: Electrical and Computer Engineering, Department of, 2016, University of Houston

The goal of this thesis is to develop a data-driven, label consistent, and dictionary learning based framework that can be applied on a variety of signal analysis problems. Current methods based on analytical models do not adequately take the variability within and across datasets into consideration when designing signal analysis algorithms. This variability can be added as a morphological constraint to improve the signal analysis algorithms. In particular, this work focuses on three different applications: 1) we present a method for large-scale automated three-dimensional (3-D) reconstruction and profiling of microglia populations in extended regions of brain tissue for quantifying arbor morphology, sensing activation states, and analyzing the spatial distributions of cell activation patterns in tissue; this work provided an opportunity to profile the distribution of microglia in the controlled and device implanted brain. 2) we present a novel morphological constrained spectral unmixing (MCSU) algorithm that combines the spectral and morphological cues in the multispectral image data cube to improve the unmixing quality, this work provided an opportunity to identify new therapeutic opportunities for pancreatic ductal adenocarcinoma (PDAC) from the images collected from humans; and finally, 3) we developed a framework to analyze neuronal response from electroencephalography (EEG) datasets acquired from the infants ranging from 6-24 months. We demonstrated that combining different frequency bands from different spatial locations, yields better classification results, instead of the traditional approach where either one or two frequency bands are used. Using an adaptation of Tibshirani’s Sparse Group LASSO algorithm, we uncovered different spatial and bio markers for understanding a human infant’s brain. These bio-markers can be used for developmental stages of infants and further analysis is required to study the clinical aspects of infant’s social and cognitive development. This work establishes the fundamental mathematical basis for the next generation of algorithms that can leverage the morphological cues from the biological datasets. The algorithm has been embedded into the open source FARSIGHT toolkit with an intuitive graphical user interface. Advisors/Committee Members: Roysam, Badrinath (advisor), Contreras-Vidal, Jose Luis (advisor), Shih, Wei-Chuan (committee member), Mayerich, David (committee member), Leasure, J. Leigh (committee member), Burks,, Jared (committee member).

Subjects/Keywords: Machine Learning; Image analysis; EEG analysis; Image processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-7613-6444. (2016). Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3531

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-7613-6444. “Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets.” 2016. Thesis, University of Houston. Accessed June 16, 2019. http://hdl.handle.net/10657/3531.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-7613-6444. “Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets.” 2016. Web. 16 Jun 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7613-6444. Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets. [Internet] [Thesis]. University of Houston; 2016. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10657/3531.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-7613-6444. Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets. [Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/3531

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

.