Download or read book Dimension Reduction Computational Complexity and Information written by Sanford Weisberg and published by . This book was released on 1998 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Open Problems in Spectral Dimensionality Reduction written by Harry Strange and published by Springer Science & Business Media. This book was released on 2014-01-07 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects or measurements whilst retaining important information. Spectral dimensionality reduction is one such tool for the data processing pipeline. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.
Download or read book Computational Complexity written by Sanjeev Arora and published by Cambridge University Press. This book was released on 2009-04-20 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Download or read book Modern Dimension Reduction written by Philip D. Waggoner and published by Cambridge University Press. This book was released on 2021-08-05 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.
Download or read book Hyperspectral Data Dimensionality Reduction and Applications written by Haleh Safavi and published by . This book was released on 2013 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dimensionality Reduction (DR) is a commonly used preprocessing technique to reduce data volumes to accomplish various tasks such as data compression, communication, transmission. Unfortunately, it also comes at a price which has two challenging issues needed to be resolved. One is that it must know the number of dimensions, q needed to be retained after DR a priori. The other is how to preserve desired data information through DR process. This dissertation develops Projection Pursuit (PP)-based Progressive Dimensionality Reduction (PDR) to address these two issues. To resolve the second issue, the PP-based DR (PP-DR) extends two well-known components analysis techniques, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) via a Projection Index which can be designed to capture desired data information. The resulting PP-DR is called PIPP-DR which includes PCA and ICA as it special cases with PI specified by data variance and mutual information where the PCA-generated Principal Components (PCs) and ICA-generated Independent Components (ICs) are also extended to Projection Index components (PICs). In order to deal with the first issue the PDR is particularly developed to by ranking PICs, in the sense of information prioritization measured by a specific criterion so the DR can be performed in a forward manner by dimensionality expansion or in a backward manner by dimensionality reduction progressively without appealing for the knowledge about the value of q. However, in order to reduce computational complexity a newly developed concept called Virtual Dimensionality (VD) can be used to lower bound and upper bound on the q, which are the VD and twice VD respectively. Since the value of q generally varies with different applications, the PDR is further extended to Dynamic Dimensionality Reduction (DDR) which allows users adapt the value of the q to meet various applications. To avoid repeatedly re-implementing DR as the traditional DR techniques do, the DDR makes use of PDR to adapt various applications. For illustrative purposes, three applications in hyperspectral data exploitation, land cover/use classification, Linear Spectral Mixture Analysis (LSMA) for spectral unmixing and endmember extraction are used for demonstration. Experimental results show that adapting the value of q to perform DDR is much more effective than traditional DR using a fixed value of q in all different applications.
Download or read book Lecture Notes in Real Time Intelligent Systems written by Jolanta Mizera-Pietraszko and published by Springer. This book was released on 2017-08-07 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent computing refers greatly to artificial intelligence with the aim at making computer to act as a human. This newly developed area of real-time intelligent computing integrates the aspect of dynamic environments with the human intelligence. This book presents a comprehensive practical and easy to read account which describes current state-of-the art in designing and implementing real-time intelligent computing to robotics, alert systems, IoT, remote access control, multi-agent systems, networking, mobile smart systems, crowd sourcing, broadband systems, cloud computing, streaming data and many other applications areas. The solutions discussed in this book will encourage the researchers and IT professional to put the methods into their practice.
Download or read book Machine Learning Applications written by Rik Das and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-04-20 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Download or read book Computational Science ICCS 2020 written by Valeria V. Krzhizhanovskaya and published by Springer Nature. This book was released on 2020-06-18 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.
Download or read book Nonlinear Dimensionality Reduction written by John A. Lee and published by Springer Science & Business Media. This book was released on 2007-10-31 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. The text provides a lucid summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all described from a unifying point of view, which helps to highlight their respective strengths and shortcomings. The presentation will appeal to statisticians, computer scientists and data analysts, and other practitioners having a basic background in statistics or computational learning.
Download or read book Supervised Dimensionality Reduction for Different Learning Architectures written by Nathan H. Parrish and published by . This book was released on 2012 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: As datasets become larger and larger, there is a need for algorithms that can efficiently extract the relevant information for a given task and represent it in a concise manner. Supervised dimensionality reduction is one approach to doing this, as it reduces the input space of the data while retaining the characteristics of the data that are useful for classification. This dissertation motivates and analyzes a new supervised dimensionality reduction technique called local discriminative Gaussian (LDG) dimensionality reduction. Experiments show that LDG is fast and effective when compared to other state-of-the-art supervised linear dimensionality reduction methods. LDG is shown to also be effective in the small sample size problem, where few training examples are provided in relation to the input dimensionality of the data. LDG is then extended to the transfer learning problem, where the goal is to classify test examples drawn from a target domain distribution using training examples drawn from a source domain distribution that differs from the target domain. Another contribution of this dissertation is an algorithm that reliably classifies incomplete test data. Incomplete test data classification is useful if one wishes to classify a test sample before all of the test data is gathered, for example, if one wishes to make an early decision on time-series data. Experiments show that the proposed algorithm can classify incomplete time-series data while maintaining accuracy that is comparable to that achieved using the complete test data. Furthermore, LDG dimensionality reduction is shown to greatly reduce the computational complexity of the incomplete test data classifier.
Download or read book Recent Advances in Information and Communication Technology 2015 written by Herwig Unger and published by Springer. This book was released on 2015-06-14 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research work and results in the area of communication and information technologies. The book includes the main results of the 11th International Conference on Computing and Information Technology (IC2IT) held during July 2nd-3rd, 2015 in Bangkok, Thailand. The book is divided into the two main parts Data Mining and Machine Learning as well as Data Network and Communications. New algorithms and methods of data mining asr discussed as well as innovative applications and state-of-the-art technologies on data mining, machine learning and data networking.
Download or read book Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2018-08-21 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful. Presents a brief overview of computational intelligence paradigms and its significant role in application domains Illustrates the state-of-the-art and recent developments in the new theories and applications of CI approaches Familiarizes the reader with computational intelligence concepts and technologies that are successfully used in the implementation of cloud-centric multimedia services in massive data processing Provides new advances in the fields of CI for bio-engineering application
Download or read book Nonlinear Estimation and Classification written by David D. Denison and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
Download or read book Preference Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2010-11-19 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Download or read book Knowledge Discovery and Data Mining written by O. Maimon and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).
Download or read book Regression for Categorical Data written by Gerhard Tutz and published by Cambridge University Press. This book was released on 2011-11-21 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.
Download or read book Theoretical Computer Science written by Lian Li and published by Springer. This book was released on 2018-09-25 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the National Conference of Theoretical Computer Science, NCTCS 2018, held in Shanghai, China, in October 2018. The 11 full papers presented were carefully reviewed and selected from 31 submissions. They present relevant trends of current research in the area of algorithms and complexity, software theory and method, data science and machine learning theory.
