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  • About
  • 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

Super-resolution methods for fluorescence microscopy

Mandula, Ondrej January 2013 (has links)
Fluorescence microscopy is an important tool for biological research. However, the resolution of a standard fluorescence microscope is limited by diffraction, which makes it difficult to observe small details of a specimen’s structure. We have developed two fluorescence microscopy methods that achieve resolution beyond the classical diffraction limit. The first method represents an extension of localisation microscopy. We used nonnegative matrix factorisation (NMF) to model a noisy dataset of highly overlapping fluorophores with intermittent intensities. We can recover images of individual sources from the optimised model, despite their high mutual overlap in the original dataset. This allows us to consider blinking quantum dots as bright and stable fluorophores for localisation microscopy. Moreover, NMF allows recovery of sources each having a unique shape. Such a situation can arise, for example, when the sources are located in different focal planes, and NMF can potentially be used for three dimensional superresolution imaging. We discuss the practical aspects of applying NMF to real datasets, and show super-resolution images of biological samples labelled with quantum dots. It should be noted that this technique can be performed on any wide-field epifluorescence microscope equipped with a camera, which makes this super-resolution method very accessible to a wide scientific community. The second optical microscopy method we discuss in this thesis is a member of the growing family of structured illumination techniques. Our main goal is to apply structured illumination to thick fluorescent samples generating a large out-of-focus background. The out-of-focus fluorescence background degrades the illumination pattern, and the reconstructed images suffer from the influence of noise. We present a combination of structured illumination microscopy and line scanning. This technique reduces the out-of-focus fluorescence background, which improves the quality of the illumination pattern and therefore facilitates reconstruction. We present super-resolution, optically sectioned images of a thick fluorescent sample, revealing details of the specimen’s inner structure. In addition, in this thesis we also discuss a theoretical resolution limit for noisy and pixelated data. We correct a previously published expression for the so-called fundamental resolution measure (FREM) and derive FREM for two fluorophores with intermittent intensity. We show that the intensity intermittency of the sources (observed for quantum dots, for example) can increase the “resolution” defined in terms of FREM.
2

Nonnegative matrix factorization algorithms and applications

Ho, Ngoc-Diep 09 June 2008 (has links)
Data-mining has become a hot topic in recent years. It consists of extracting relevant information or structures from data such as: pictures, textual material, networks, etc. Such information or structures are usually not trivial to obtain and many techniques have been proposed to address this problem, including Independent Component Analysis, Latent Sematic Analysis, etc. Nonnegative Matrix Factorization is yet another technique that relies on the nonnegativity of the data and the nonnegativity assumption of the underlying model. The main advantage of this technique is that nonnegative objects are modeled by a combination of some basic nonnegative parts, which provides a physical interpretation of the construction of the objects. This is an exclusive feature that is known to be useful in many areas such as Computer Vision, Information Retrieval, etc. In this thesis, we look at several aspects of Nonnegative Matrix Factorization, focusing on numerical algorithms and their applications to different kinds of data and constraints. This includes Tensor Nonnegative Factorization, Weighted Nonnegative Matrix Factorization, Symmetric Nonnegative Matrix Factorization, Stochastic Matrix Approximation, etc. The recently proposed Rank-one Residue Iteration (RRI) is the common thread in all of these factorizations. It is shown to be a fast method with good convergence properties which adapts well to many situations.
3

CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION

Thapa, Nirmal 01 January 2013 (has links)
Data are valuable assets to any organizations or individuals. Data are sources of useful information which is a big part of decision making. All sectors have potential to benefit from having information. Commerce, health, and research are some of the fields that have benefited from data. On the other hand, the availability of the data makes it easy for anyone to exploit the data, which in many cases are private confidential data. It is necessary to preserve the confidentiality of the data. We study two categories of privacy: Data Value Hiding and Data Pattern Hiding. Privacy is a huge concern but equally important is the concern of data utility. Data should avoid privacy breach yet be usable. Although these two objectives are contradictory and achieving both at the same time is challenging, having knowledge of the purpose and the manner in which it will be utilized helps. In this research, we focus on some particular situations for clustering and classification problems and strive to balance the utility and privacy of the data. In the first part of this dissertation, we propose Nonnegative Matrix Factorization (NMF) based techniques that accommodate constraints defined explicitly into the update rules. These constraints determine how the factorization takes place leading to the favorable results. These methods are designed to make alterations on the matrices such that user-specified cluster properties are introduced. These methods can be used to preserve data value as well as data pattern. As NMF and K-means are proven to be equivalent, NMF is an ideal choice for pattern hiding for clustering problems. In addition to the NMF based methods, we propose methods that take into account the data structures and the attribute properties for the classification problems. We separate the work into two different parts: linear classifiers and nonlinear classifiers. We propose two different solutions based on the classifiers. We study the effect of distortion on the utility of data. We propose three distortion measurement metrics which demonstrate better characteristics than the traditional metrics. The effectiveness of the measures is examined on different benchmark datasets. The result shows that the methods have the desirable properties such as invariance to translation, rotation, and scaling.
4

Topic Analysis of Hidden Trends in Patented Features Using Nonnegative Matrix Factorization

Lin, Yicong 01 January 2016 (has links)
Intellectual property has gained more attention in recent decades because innovations have become one of the most important resources. This paper implements a probabilistic topic model using nonnegative matrix factorization (NMF) to discover some of the key elements in computer patent, as the industry grew from 1990 to 2009. This paper proposes a new “shrinking model” based on NMF and also performs a close examination of some variations of the base model. Note that rather than studying the strategy to pick the optimized number of topics (“rank”), this paper is particularly interested in which factorization (including different kinds of initiation) methods are able to construct “topics” with the best quality given the predetermined rank. Performing NMF to the description text of patent features, we observe key topics emerge such as “platform” and “display” with strong presence across all years but we also see other short-lived significant topics such as “power” and “heat” which signify the saturation of the industry.
5

Examination of Initialization Techniques for Nonnegative Matrix Factorization

Frederic, John 21 November 2008 (has links)
While much research has been done regarding different Nonnegative Matrix Factorization (NMF) algorithms, less time has been spent looking at initialization techniques. In this thesis, four different initializations are considered. After a brief discussion of NMF, the four initializations are described and each one is independently examined, followed by a comparison of the techniques. Next, each initialization's performance is investigated with respect to the changes in the size of the data set. Finally, a method by which smaller data sets may be used to determine how to treat larger data sets is examined.
6

Nonnegative matrix factorization for clustering

Kuang, Da 27 August 2014 (has links)
This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and efficient clustering method. Clustering is one of the fundamental tasks in machine learning. It is useful for unsupervised knowledge discovery in a variety of applications such as text mining and genomic analysis. NMF is a dimension reduction method that approximates a nonnegative matrix by the product of two lower rank nonnegative matrices, and has shown great promise as a clustering method when a data set is represented as a nonnegative data matrix. However, challenges in the widespread use of NMF as a clustering method lie in its correctness and efficiency: First, we need to know why and when NMF could detect the true clusters and guarantee to deliver good clustering quality; second, existing algorithms for computing NMF are expensive and often take longer time than other clustering methods. We show that the original NMF can be improved from both aspects in the context of clustering. Our new NMF-based clustering methods can achieve better clustering quality and run orders of magnitude faster than the original NMF and other clustering methods. Like other clustering methods, NMF places an implicit assumption on the cluster structure. Thus, the success of NMF as a clustering method depends on whether the representation of data in a vector space satisfies that assumption. Our approach to extending the original NMF to a general clustering method is to switch from the vector space representation of data points to a graph representation. The new formulation, called Symmetric NMF, takes a pairwise similarity matrix as an input and can be viewed as a graph clustering method. We evaluate this method on document clustering and image segmentation problems and find that it achieves better clustering accuracy. In addition, for the original NMF, it is difficult but important to choose the right number of clusters. We show that the widely-used consensus NMF in genomic analysis for choosing the number of clusters have critical flaws and can produce misleading results. We propose a variation of the prediction strength measure arising from statistical inference to evaluate the stability of clusters and select the right number of clusters. Our measure shows promising performances in artificial simulation experiments. Large-scale applications bring substantial efficiency challenges to existing algorithms for computing NMF. An important example is topic modeling where users want to uncover the major themes in a large text collection. Our strategy of accelerating NMF-based clustering is to design algorithms that better suit the computer architecture as well as exploit the computing power of parallel platforms such as the graphic processing units (GPUs). A key observation is that applying rank-2 NMF that partitions a data set into two clusters in a recursive manner is much faster than applying the original NMF to obtain a flat clustering. We take advantage of a special property of rank-2 NMF and design an algorithm that runs faster than existing algorithms due to continuous memory access. Combined with a criterion to stop the recursion, our hierarchical clustering algorithm runs significantly faster and achieves even better clustering quality than existing methods. Another bottleneck of NMF algorithms, which is also a common bottleneck in many other machine learning applications, is to multiply a large sparse data matrix with a tall-and-skinny dense matrix. We use the GPUs to accelerate this routine for sparse matrices with an irregular sparsity structure. Overall, our algorithm shows significant improvement over popular topic modeling methods such as latent Dirichlet allocation, and runs more than 100 times faster on data sets with millions of documents.
7

Détection de changements en imagerie hyperspectrale : une approche directionnelle / Change detection in hyperspectral imagery : a directional approach

Brisebarre, Godefroy 24 November 2014 (has links)
L’imagerie hyperspectrale est un type d’imagerie émergent qui connaît un essor important depuis le début des années 2000. Grâce à une structure spectrale très fine qui produit un volume de donnée très important, elle apporte, par rapport à l’imagerie visible classique, un supplément d’information pouvant être mis à profit dans de nombreux domaines d’exploitation. Nous nous intéressons spécifiquement à la détection et l’analyse de changements entre deux images de la même scène, pour des applications orientées vers la défense.Au sein de ce manuscrit, nous commençons par présenter l’imagerie hyperspectrale et les contraintes associées à son utilisation pour des problématiques de défense. Nous présentons ensuite une méthode de détection et de classification de changements basée sur la recherche de directions spécifiques dans l’espace généré par le couple d’images, puis sur la fusion des directions proches. Nous cherchons ensuite à exploiter l’information obtenue sur les changements en nous intéressant aux possibilités de dé-mélange de séries temporelles d’images d’une même scène. Enfin, nous présentons un certain nombre d’extensions qui pourront être réalisées afin de généraliser ou améliorer les travaux présentés et nous concluons. / Hyperspectral imagery is an emerging imagery technology which has known a growing interest since the 2000’s. This technology allows an impressive growth of the data registered from a specific scene compared to classical RGB imagery. Indeed, although the spatial resolution is significantly lower, the spectral resolution is very small and the covered spectral area is very wide. We focus on change detection between two images of a given scene for defense oriented purposes.In the following, we start by introducing hyperspectral imagery and the specificity of its exploitation for defence purposes. We then present a change detection and analysis method based on the search for specifical directions in the space generated by the image couple, followed by a merging of the nearby directions. We then exploit this information focusing on theunmixing capabilities of multitemporal hyperspectral data. Finally, we will present a range of further works that could be done in relation with our work and conclude about it.
8

Studies on Matrix Eigenvalue Problems in Terms of Discrete Integrable Systems / 離散可積分系による行列固有値問題の研究

Akaiwa, Kanae 24 September 2015 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19341号 / 情博第593号 / 新制||情||103(附属図書館) / 32343 / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 中村 佳正, 教授 矢ケ崎 一幸, 教授 西村 直志 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
9

Block Coordinate Descent for Regularized Multi-convex Optimization

Xu, Yangyang 16 September 2013 (has links)
This thesis considers regularized block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. I review some of its interesting examples and propose a generalized block coordinate descent (BCD) method. The generalized BCD uses three different block-update schemes. Based on the property of one block subproblem, one can freely choose one of the three schemes to update the corresponding block of variables. Appropriate choices of block-update schemes can often speed up the algorithm and greatly save computing time. Under certain conditions, I show that any limit point satisfies the Nash equilibrium conditions. Furthermore, I establish its global convergence and estimate its asymptotic convergence rate by assuming a property based on the Kurdyka-{\L}ojasiewicz inequality. As a consequence, this thesis gives a global linear convergence result of cyclic block coordinate descent for strongly convex optimization. The proposed algorithms are adapted for factorizing nonnegative matrices and tensors, as well as completing them from their incomplete observations. The algorithms were tested on synthetic data, hyperspectral data, as well as image sets from the CBCL, ORL and Swimmer databases. Compared to the existing state-of-the-art algorithms, the proposed algorithms demonstrate superior performance in both speed and solution quality.
10

Dictionary learning methods for single-channel source separation

Lefèvre, Augustin 03 October 2012 (has links) (PDF)
In this thesis we provide three main contributions to blind source separation methods based on NMF. Our first contribution is a group-sparsity inducing penalty specifically tailored for Itakura-Saito NMF. In many music tracks, there are whole intervals where only one source is active at the same time. The group-sparsity penalty we propose allows to blindly indentify these intervals and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF. Our second contribution is an online algorithm for Itakura-Saito NMF that allows to learn dictionaries on very large audio tracks. Indeed, the memory complexity of a batch implementation NMF grows linearly with the length of the recordings and becomes prohibitive for signals longer than an hour. In contrast, our online algorithm is able to learn NMF on arbitrarily long signals with limited memory usage. Our third contribution deals user informed NMF. In short mixed signals, blind learning becomes very hard and sparsity do not retrieve interpretable dictionaries. Our contribution is very similar in spirit to inpainting. It relies on the empirical fact that, when observing the spectrogram of a mixture signal, an overwhelming proportion of it consists in regions where only one source is active. We describe an extension of NMF to take into account time-frequency localized information on the absence/presence of each source. We also investigate inferring this information with tools from machine learning.

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