Graph Based Semi Supervised Learning
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Graph Based Semi Supervised Learning
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Author : Amarnag Lipovetzky
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Graph Based Semi Supervised Learning written by Amarnag Lipovetzky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index
Analysis And Application Of Graph Based Semi Supervised Learning Methods
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Author : XIYANG LUO
language : en
Publisher:
Release Date : 2018
Analysis And Application Of Graph Based Semi Supervised Learning Methods written by XIYANG LUO and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In recent years, the need for pattern recognition and data analysis has grown exponentially in various fields of scientific research. My research is centered around graph Laplacian based techniques for image processing and machine learning. Three papers pertaining to this theme will be presented in this thesis.The first work is an application of graph Laplacian regularization to the problem of convolutional sparse coding. The additional regularization improves the robustness of the sparse representation with respect to noise, and has empirically shown to improve the performance of denoising on several well-known images. Efficient algorithms for computing the eigen-decomposition of the graph Laplacian were also incorporated to the solver for fast implementations of the method.The second piece of work studies the convergence of the graph Allen-Cahn scheme. A technique inspired by the maximum principle for the heat equation is used to show stability of the convex-splitting numeric scheme. This coupled with techniques from convex optimization allows for a proof of convergence under an a-posteriori condition. The analysis is then generalized to handle spectral trunction, a common method to save computational cost, and also to the case of multi-class classification. In particular, the results for spectral trunction are drastically different from that of the original scheme in the worst case, but does not present itself in practical applications.The third piece of work combines two fields of research, uncertainty quantification, and semi-supervised learning on graphs. The work presents a unified Bayesian framework thatincorporates most previous methods for graph-based semi-supervised learning. A Bayesianframework allows for the computation of uncertainty for certain quantities under the pos-terior distribution. We show via solid numerical evidence that for a few carefully designedquantities, the expectations computed under the posterior yields meaningful notions of un-certainty for the classification problem. Efficient numerical methods were also devised tomake possible the evaluation of these quantities for large scale graphs.
Introduction To Semi Supervised Learning
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Author : Xiaojin Zhu
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009
Introduction To Semi Supervised Learning written by Xiaojin Zhu and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook
Minimal Labels Maximum Gain
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Author : Philip Sellars
language : en
Publisher:
Release Date : 2021
Minimal Labels Maximum Gain written by Philip Sellars and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Active Learning And Uncertainty In Graph Based Semi Supervised Learning
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Author : Kevin Miller
language : en
Publisher:
Release Date : 2022
Active Learning And Uncertainty In Graph Based Semi Supervised Learning written by Kevin Miller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
We present various results and methods for measuring uncertainty and applying active learning to graph-based semi-supervised learning, as well as a graph-dependent result for generalization of decentralized federated learning. The first piece of work presents an analysis of graph-based semi-supervised learning in the framework of Bayesian inverse problems; we prove posterior consistency of the corresponding Bayesian posterior distribution under a clustering model that accounts for overlap between clusters. The second and third pieces of work introduce and apply a graph-based method for selecting informative points for use in active learning. We present a computationally efficient framework for this active learning method and present empirical results on both hyperspectral and synthetic aperture radar datasets. The final piece of work provides an analysis of graph structure dependent generalization guarantees for decentralized federated learning. Through both theoretical analysis and empirical results, we demonstrate that expander graphs are in a sense optimally efficient for balancing communication cost as well as mixing properties of the associated graph.
Semi Supervised Learning
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Author : Olivier Chapelle
language : en
Publisher: MIT Press
Release Date : 2010-01-22
Semi Supervised Learning written by Olivier Chapelle and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-22 with Computers categories.
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Graph Based Semi Supervised Learning In Computer Vision
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Author : Ning Huang
language : en
Publisher:
Release Date : 2009
Graph Based Semi Supervised Learning In Computer Vision written by Ning Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computer vision categories.
Machine learning from previous examples or knowledge is a key element in many image processing and pattern recognition tasks, e.g. clustering, segmentation, stereo matching, optical flow, tracking and object recognition. Acquiring that knowledge frequently requires human labeling of large data sets, which can be difficult and time-consuming to obtain. One way to ameliorate this task is to use Semi-supervised Learning (SSL), which combines both labeled and raw data and incorporates both global consistency (points in the same cluster are likely to have the same label) and local smoothness (nearby points are likely to have the same label). There are a number of vision tasks that can be solved efficiently and accurately using SSL. SSL has been applied extensively in clustering and image segmentation. In this dissertation, we will show that it is also suitable for stereo matching, optical flow and tracking problems. Our novel algorithm has converted the stereo matching problem into a multi-label semi-supervised learning one. It is similar to a diffusion process, and we will show our approach has a closed-form solution for the multi-label problem. It sparks a new direction from the traditional energy minimization approach, such as Graph Cut or Belief Propagation. The occlusion area is detected using the matching confidence level, and solved with local fitting. Our results have been applied in the Middlebury Stereo database, and are within the top 20 best results in terms of accuracy and is considerably faster than the competing approaches. We have also adapted our algorithm, and demonstrated its performance on optical flow problems. Again, our results are compared with the ground truth and state of the art on the Middlebury Flow database, and its advantages in accuracy as well as speed are demonstrated. The above algorithm is also being used in our current NSF sponsored project, an Automated, Real-Time Identification and Monitoring Instrument for Reef Fish Communities, whose goal is to track and recognize tropical fish, initially in an aquarium and ultimately on a coral reef. Our approach, which combines background subtraction and optical flow, automatically finds the correct outline of multiple fish species in the field of view, and tracks the contour reliably over consecutive frames. Currently, near real-time results are being achieved, with a processing frame rate of 3-5 fps. The recent progress in semi-supervised learning applied to image segmentation is also briefly reviewed.
Semi Supervised Learning With Side Information
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Author : Yi Liu
language : en
Publisher:
Release Date : 2007
Semi Supervised Learning With Side Information written by Yi Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computer science categories.
Cognitive Analytics Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2020-03-06
Cognitive Analytics Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-06 with Science categories.
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.
Graph Based Semi Supervised Learning In Acoustic Modeling For Automatic Speech Recognition
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Author : Yuzong Liu
language : en
Publisher:
Release Date : 2016
Graph Based Semi Supervised Learning In Acoustic Modeling For Automatic Speech Recognition written by Yuzong Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Acoustic models require a large amount of training data. However, lots of labor is required to annotate the training data for automatic speech recognition. More importantly, the performance of the acoustic model could degenerate during test time, where the conditions of test data differ from the training data in speaker characteristics, channel and recording environment. To compensate for the deviation between training and test conditions, we investigate a graph-based semi-supervised learning approach to acoustic modeling in automatic speech recognition. Graph-based semi-supervised learning (SSL) is a widely used semi-supervised learning method in which the labeled data and unlabeled data are jointly represented as a weighted graph, and the information is propagated from the labeled data to the unlabeled data. The key assumption that graph-based SSL makes is that data samples lie on a low dimensional manifold, where samples that are close to each other are expected to have the same class label. More importantly, by exploiting the relationship between training and test samples, graph-based SSL implicitly adapts to the test data. In this thesis, we address several key challenges in applying graph-based SSL to acoustic modeling. We first investigate and compare several state-of-the-art graph-based SSL algorithms on a benchmark dataset. In addition, we propose novel graph construction methods that allow graph-based SSL to handle variable-length input features. We next investigate the efficacy of graph-based SSL in context of a fully-fledged DNN-based ASR system. We compare two different integration frameworks for graph-based learning. First, we propose a lattice-based late integration framework that combines graph-based SSL with the DNN-based acoustic modeling and evaluate the framework on continuous word recognition tasks. Second, we propose an early integration framework using neural graph embeddings and compare two different neural graph embedding features that capture the information of the manifold at different levels. The embedding features are used as input to a DNN system and are shown to outperform the conventional acoustic feature inputs on several medium-to-large vocabulary conversational speech recognition tasks.