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Author: Bogdan Dumitrescu
Publisher: Springer
ISBN: 3319786741
Category : Technology & Engineering
Languages : en
Pages : 289
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Book Description
This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
Author: Bogdan Dumitrescu
Publisher: Springer
ISBN: 3319786741
Category : Technology & Engineering
Languages : en
Pages : 289
Get Book Here
Book Description
This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
Author: Qiang Zhang
Publisher: Springer Nature
ISBN: 303102253X
Category : Technology & Engineering
Languages : en
Pages : 133
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Book Description
The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.
Author: Huan Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 332
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Book Description
Author: Sherin Mary Mathews
Publisher:
ISBN: 9781369681222
Category : Algorithms
Languages : en
Pages : 119
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Book Description
Dictionary and deep learning algorithms facilitate efficient signal representations, thereby offering tremendous representational power along with achieving good recognition rates in real-world machine learning problems. In this dissertation, we present three dictionary learning approaches and a deep learning framework for classification tasks related to remote health monitoring systems. ☐ This dissertation presents a more robust class specific centralized dictionary learning method to solve the wearable sensor-based physical activity classification problem. Inspired by experiments that achieved high recognition rates using a few representative samples on high dimensional data, we explore the physical activity recognition signals from wearable sensors and propose a dictionary pair learning-based framework for human physical activity monitoring and recognition. The essential strategy involves integrating the class specific centralized regularizer term into the dictionary pair learning objective function and efficiently optimizing the objective function by combining the alternating direction method of multipliers and the l1 -- ls minimization method. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby enhancing the classification performance. Experimental results show that the classifiers built in this framework achieve higher recognition rate over four activity recognition tasks and outperforms state-of-the-art methods. ☐ Physical activity recognition involves variations in different walking styles and human body movements which result in the erroneous classification of similar activities. To address this issue, we present a correntropy induced dictionary pair learning framework to achieve improved recognition. In particular, the dictionary pair learning algorithm developed based on the maximum correntropy criterion is much more insensitive to outliers. A combination of alternating direction method of multipliers and an iteratively reweighted method is employed to approximately minimize the objective function. Evaluations are conducted using four activity recognition tasks and results show that the proposed classifier framework achieve enhanced performance compared to the state-of-the-art recognition systems. ☐ Although classification accuracy is enhanced using state-of-art classifiers, actual recognition performance tends to fall off when distinguishing a large number of similar activities. To this end, we propose and evaluate methods for analyzing hierarchical and sequentially structured human activities, designed to scale activity recognition by creating a hierarchical cluster of activity labels. Instead of using a single classifier to distinguish between large numbers of activities, we propose a hierarchy of classifiers, each of which distinguishes between child nodes at a particular location in the hierarchy. We hypothesize that building such a hierarchy of activity will improve recognition performance over that of the at classifier model. We validate the effectiveness of our proposed model by employing it on two standard activity recognition datasets, which include a large set of similar physical activities. The results of hierarchical structure modeling furnish evidence that decomposing the problem leads to more accurate specialized classifiers. ☐ This dissertation also applies deep learning methodology to the classification of single-lead electrocardiogram (ECG) signals. State of-the-art automatic ECG recognition systems often rely on a pattern-matching framework thereby requiring high sampling rates and burdensome computational times to classify arrhythmias. Deep learning networks represent a high level of abstraction showcasing its tremendous representational power. Consequently, to enable implementation in real time, we develop a deep learning framework that includes Restricted Boltzmann Machine and Deep Belief Networks for ECG classification with lower computational time, making it a highly practical option in a clinical setting.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Irina Rish
Publisher: CRC Press
ISBN: 1439828695
Category : Business & Economics
Languages : en
Pages : 255
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Book Description
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.
Author: Bojana Begovic
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Modern era of signal processing has developed many technical tools for recording and processing large and growing amount of data together with algorithms specialised for data analysis. This gives rise to new challenges in terms of data processing and modelling data representation. Fields ranging from experimental sciences, astronomy, computer vision,neuroscience mobile networks etc., are all in constant search for scalable and efficient data processing tools which would enable more effective analysis of continuous video streams containing millions of pixels. Therefore, the question of digital signal representation is still of high importance, despite the fact that it has been the topic of a significant amount of work in the past. Moreover, developing new data processing methods also affects the quality of everyday life, where devices such as CCD sensors from digital cameras or cell phones are intensively used for entertainment purposes. Specifically, one of the novel processing tools is signal sparse coding which represents signals as linear combinations of a few representational basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and regularisation in inverse problems, feature extraction, and more. In this thesis we introduce and study a particular signal representation denoted as the scalable sparse coding. It is based on a novel design for the dictionary learning algorithm, which has proven to be effective for scalable sparse representation of many modalities such as high motion video sequences, natural and solar images. The proposed algorithm is built upon the foundation of the K-SVD framework originally designed to learn non-scalable dictionaries for natural images. The scalable dictionary learning design is mainly motivated by the main perception characteristics of the Human Visual System (HVS) mechanism. Specifically, its core structure relies on the exploitation of the spatial high-frequency image components and contrast variations in order to achieve visual scene objects identification at all scalable levels. The implementation of HVS properties is carried out by introducing a semi-random Morphological Component Analysis (MCA) based initialisation of the scalable dictionary and the regularisation of its atom's update mechanism. Subsequently, this enables scalable sparse image reconstruction. In general, dictionary learning for sparse representations leads to state-of-the-art image restoration results for several different problems in the field of image processing. Experiments in this thesis show that these are equally achievable by accommodating all dictionary elements to tailor the scalable data representation and reconstruction, hence modelling data that admit sparse representation in a novel manner. Furthermore, achieved results demonstrateand validate the practicality of the proposed scheme making it a promising candidate for many practical applications involving both time scalable display, denoising and scalable compressive sensing (CS). Performed simulations include scalable sparse recovery for representation of static and dynamic data changing over time such as video sequences and natural images. Lastly, we contribute novel approaches for scalable denoising and contrast enhancement (CE), applied on solar images corrupted with pixel-dependent Poisson and zero-mean additive white Gaussian noise. Given that solar data contain noise introduced by charge-coupled devices within the on-board acquisition system these artefacts, prior to image analysis, have to be removed. Thus, novel image denoising and contrast enhancement methods are necessary for solar preprocessing.
Author: Michael Elad
Publisher: Springer Science & Business Media
ISBN: 1441970118
Category : Mathematics
Languages : en
Pages : 376
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Book Description
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Author: Baiyang Liu
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 79
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Book Description
This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1785884514
Category : Computers
Languages : en
Pages : 352
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Book Description
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
