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• The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

#### Greedy Strategies for Convex Minimization

Nguyen, Hao Thanh 16 December 2013 (has links)
We have investigated two greedy strategies for finding an approximation to the minimum of a convex function E, defined on a Hilbert space H. We have proved convergence rates for a modification of the orthogonal matching pursuit and its weak version under suitable conditions on the objective function E. These conditions involve the behavior of the moduli of smoothness and the modulus of uniform convexity of E.
2

#### The selection of compounds for screening in pharmaceutical research

Harper, Gavin January 1999 (has links)
No description available.
3

#### A Practical Comprehensive Approach to PMU Placement for Full Observability

Altman, James Ross 27 March 2008 (has links)
In recent years, the placement of phasor measurement units (PMUs) in electric transmission systems has gained much attention. Engineers and mathematicians have developed a variety of algorithms to determine the best locations for PMU installation. But often these placement algorithms are not practical for real systems and do not cover the whole process. This thesis presents a strategy that is practical and addresses three important topics: system preparation, placement algorithm, and installation scheduling. To be practical, a PMU strategy should strive for full observability, work well within the heterogeneous nature of power system topology, and enable system planners to adapt the strategy to meet their unique needs and system configuration. Practical considerations for the three placement topics are discussed, and a specific strategy based on these considerations is developed and demonstrated on real transmission system models. / Master of Science
4

#### Vehicle sensor-based pedestrian position identification in V2V environment

Huang, Zhi 03 December 2016 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents a method to accurately determine the location and amount of pedestrians detected by different vehicles equipped with a Pedestrian Autonomous Emergency Braking (PAEB) system, taking into consideration the inherent inaccuracy of the pedestrian sensing from these vehicles. In the thesis, a mathematical model of the pedestrian information generated by the PAEB system in the V2V network is developed. The Greedy-Medoids clustering algorithm and constrained hierarchical clustering are applied to recognize and reconstruct actual pedestrians, which enables a subject vehicle to approximate the number of the pedestrians and their estimated locations from a larger number of pedestrian alert messages received from many nearby vehicles through the V2V network and the subject vehicle itself. The proposed methods determines the possible number of actual pedestrians by grouping the nearby pedestrians information broadcasted by different vehicles and considers them as one pedestrian. Computer simulations illustrate the effectiveness and applicability of the proposed methods. The results are more integrated and accurate information for vehicle Autonomous Emergency Braking (AEB) systems to make better decisions earlier to avoid crashing into pedestrians.
5

#### Greedy structure learning of Markov Random Fields

Johnson, Christopher Carroll 04 November 2011 (has links)
Probabilistic graphical models are used in a variety of domains to capture and represent general dependencies in joint probability distributions. In this document we examine the problem of learning the structure of an undirected graphical model, also called a Markov Random Field (MRF), given a set of independent and identically distributed (i.i.d.) samples. Specifically, we introduce an adaptive forward-backward greedy algorithm for learning the structure of a discrete, pairwise MRF given a high dimensional set of i.i.d. samples. The algorithm works by greedily estimating the neighborhood of each node independently through a series of forward and backward steps. By imposing a restricted strong convexity condition on the structure of the learned graph we show that the structure can be fully learned with high probability given $n=\Omega(d\log (p))$ samples where $d$ is the dimension of the graph and $p$ is the number of nodes. This is a significant improvement over existing convex-optimization based algorithms that require a sample complexity of $n=\Omega(d^2\log(p))$ and a stronger irrepresentability condition. We further support these claims with an empirical comparison of the greedy algorithm to node-wise $\ell_1$-regularized logistic regression as well as provide a real data analysis of the greedy algorithm using the Audioscrobbler music listener dataset. The results of this document provide an additional representation of work submitted by A. Jalali, C. Johnson, and P. Ravikumar to NIPS 2011. / text
6

#### Greedy algorithms for multi-channel sparse recovery

Determe, Jean-François 16 January 2018 (has links)
During the last decade, research has shown compressive sensing (CS) to be a promising theoretical framework for reconstructing high-dimensional sparse signals. Leveraging a sparsity hypothesis, algorithms based on CS reconstruct signals on the basis of a limited set of (often random) measurements. Such algorithms require fewer measurements than conventional techniques to fully reconstruct a sparse signal, thereby saving time and hardware resources. This thesis addresses several challenges. The first is to theoretically understand how some parameters—such as noise variance—affect the performance of simultaneous orthogonal matching pursuit (SOMP), a greedy support recovery algorithm tailored to multiple measurement vector signal models. Chapters 4 and 5 detail novel improvements in understanding the performance of SOMP. Chapter 4 presents analyses of SOMP for noiseless measurements; using those analyses, Chapter 5 extensively studies the performance of SOMP in the noisy case. A second challenge consists in optimally weighting the impact of each measurement vector on the decisions of SOMP. If measurement vectors feature unequal signal-to-noise ratios, properly weighting their impact improves the performance of SOMP. Chapter 6 introduces a novel weighting strategy from which SOMP benefits. The chapter describes the novel weighting strategy, derives theoretically optimal weights for it, and presents both theoretical and numerical evidence that the strategy improves the performance of SOMP. Finally, Chapter 7 deals with the tendency for support recovery algorithms to pick support indices solely for mapping a particular noise realization. To ensure that such algorithms pick all the correct support indices, researchers often make the algorithms pick more support indices than the number strictly required. Chapter 7 presents a support reduction technique, that is, a technique removing from a support the supernumerary indices solely mapping noise. The advantage of the technique, which relies on cross-validation, is that it is universal, in that it makes no assumption regarding the support recovery algorithm generating the support. Theoretical results demonstrate that the technique is reliable. Furthermore, numerical evidence proves that the proposed technique performs similarly to orthogonal matching pursuit with cross-validation (OMP-CV), a state-of-the-art algorithm for support reduction. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished
7

#### Greedy Representative Selection for Unsupervised Data Analysis

Helwa, Ahmed Khairy Farahat January 2012 (has links)
In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this overwhelming amount of data stimulates a growing need to develop fast and accurate algorithms to discover useful information hidden in the data. This need is even more acute for unsupervised data, which lacks information about the categories of different instances. This dissertation addresses a crucial problem in unsupervised data analysis, which is the selection of representative instances and/or features from the data. This problem can be generally defined as the selection of the most representative columns of a data matrix, which is formally known as the Column Subset Selection (CSS) problem. Algorithms for column subset selection can be directly used for data analysis or as a pre-processing step to enhance other data mining algorithms, such as clustering. The contributions of this dissertation can be summarized as outlined below. First, a fast and accurate algorithm is proposed to greedily select a subset of columns of a data matrix such that the reconstruction error of the matrix based on the subset of selected columns is minimized. The algorithm is based on a novel recursive formula for calculating the reconstruction error, which allows the development of time and memory-efficient algorithms for greedy column subset selection. Experiments on real data sets demonstrate the effectiveness and efficiency of the proposed algorithms in comparison to the state-of-the-art methods for column subset selection. Second, a kernel-based algorithm is presented for column subset selection. The algorithm greedily selects representative columns using information about their pairwise similarities. The algorithm can also calculate a Nyström approximation for a large kernel matrix based on the subset of selected columns. In comparison to different Nyström methods, the greedy Nyström method has been empirically shown to achieve significant improvements in approximating kernel matrices, with minimum overhead in run time. Third, two algorithms are proposed for fast approximate k-means and spectral clustering. These algorithms employ the greedy column subset selection method to embed all data points in the subspace of a few representative points, where the clustering is performed. The approximate algorithms run much faster than their exact counterparts while achieving comparable clustering performance. Fourth, a fast and accurate greedy algorithm for unsupervised feature selection is proposed. The algorithm is an application of the greedy column subset selection method presented in this dissertation. Similarly, the features are greedily selected such that the reconstruction error of the data matrix is minimized. Experiments on benchmark data sets show that the greedy algorithm outperforms state-of-the-art methods for unsupervised feature selection in the clustering task. Finally, the dissertation studies the connection between the column subset selection problem and other related problems in statistical data analysis, and it presents a unified framework which allows the use of the greedy algorithms presented in this dissertation to solve different related problems.
8

#### Greedy Representative Selection for Unsupervised Data Analysis

Helwa, Ahmed Khairy Farahat January 2012 (has links)
In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this overwhelming amount of data stimulates a growing need to develop fast and accurate algorithms to discover useful information hidden in the data. This need is even more acute for unsupervised data, which lacks information about the categories of different instances. This dissertation addresses a crucial problem in unsupervised data analysis, which is the selection of representative instances and/or features from the data. This problem can be generally defined as the selection of the most representative columns of a data matrix, which is formally known as the Column Subset Selection (CSS) problem. Algorithms for column subset selection can be directly used for data analysis or as a pre-processing step to enhance other data mining algorithms, such as clustering. The contributions of this dissertation can be summarized as outlined below. First, a fast and accurate algorithm is proposed to greedily select a subset of columns of a data matrix such that the reconstruction error of the matrix based on the subset of selected columns is minimized. The algorithm is based on a novel recursive formula for calculating the reconstruction error, which allows the development of time and memory-efficient algorithms for greedy column subset selection. Experiments on real data sets demonstrate the effectiveness and efficiency of the proposed algorithms in comparison to the state-of-the-art methods for column subset selection. Second, a kernel-based algorithm is presented for column subset selection. The algorithm greedily selects representative columns using information about their pairwise similarities. The algorithm can also calculate a Nyström approximation for a large kernel matrix based on the subset of selected columns. In comparison to different Nyström methods, the greedy Nyström method has been empirically shown to achieve significant improvements in approximating kernel matrices, with minimum overhead in run time. Third, two algorithms are proposed for fast approximate k-means and spectral clustering. These algorithms employ the greedy column subset selection method to embed all data points in the subspace of a few representative points, where the clustering is performed. The approximate algorithms run much faster than their exact counterparts while achieving comparable clustering performance. Fourth, a fast and accurate greedy algorithm for unsupervised feature selection is proposed. The algorithm is an application of the greedy column subset selection method presented in this dissertation. Similarly, the features are greedily selected such that the reconstruction error of the data matrix is minimized. Experiments on benchmark data sets show that the greedy algorithm outperforms state-of-the-art methods for unsupervised feature selection in the clustering task. Finally, the dissertation studies the connection between the column subset selection problem and other related problems in statistical data analysis, and it presents a unified framework which allows the use of the greedy algorithms presented in this dissertation to solve different related problems.
9

#### Placement de graphes de tâches de grande taille sur architectures massivement multicoeurs / Mapping of large task network on manycore architecture

Berger, Karl-Eduard 08 December 2015 (has links)