Fakhri Karray

Benyamin Ghojogh

Mark Crowley

Ali Ghodsi

Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.

Elements of Dimensionality Reduction and Manifold Learning

Jong-Hwan Kim

Hyun Myung

Peter Xu

Eric T. Matson

We are facing a new technological challenge on how to store and retrieve knowledge and manipulate intelligence for autonomous services by intelligent systems which should be capable of carrying out real world tasks autonomously. To address this issue, robot researchers have been developing intelligence technology (InT) for “robots that think” which is in the focus of this book. The book covers all aspects of intelligence from perception at sensor level and reasoning at cognitive level to behavior planning at execution level for each low level segment of the machine. It also presents the technologies for cognitive reasoning, social interaction with humans, behavior generation, ability to cooperate with other robots, ambience awareness and an artificial genome that can be passed on to other robots. These technologies are to materialize cognitive intelligence, social intelligence, behavioral intelligence, collective intelligence, ambient intelligence and genetic intelligence. The book aims at serving researchers and practitioners with a timely dissemination of the recent progress on robot intelligence technology and its applications, based on a collection of papers presented at the at the 2nd International Conference on Robot Intelligence Technology and Applications (RiTA), held in Denver, USA, December 18-20, 2013.

Results from the 2nd International Conference on Robot Intelligence Technology and Applications

Robot Intelligence Technology and Applications 2

Mohamed Kamel

Hani Hagras

This book constitutes the refereed proceedings of the Third International Conference on Autonomous and Intelligent Systems, AIS 2012, held in Aveiro, Portugal, in June 2012, collocated with the International Conference on Image Analysis and Recognition, IACIAR 2012. The 31 revised full papers were carefully reviewed and selected from 48 submissions. The papers are organized in topical sections on autonomous sensors and sensor systems, autonomous systems and intelligent control with applications, intelligent fuzzy systems, intelligent robotics, intelligent knowledge management, swarm and evolutionary methods, and applications

Third International Conference, AIS 2012, Aviero, Portugal, June 25-27, 2012, Proceedings

Autonomous and Intelligent Systems

Eric T . Matson

Aurélio Campilho

Alfred Yu

This two-volume set LNCS 11662 and 11663 constitutes the refereed proceedings of the 16th International Conference on Image Analysis and Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: Image Processing; Image Analysis; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep Learning on the Edge; Recognition; Applications; Medical Imaging and Analysis Using Deep Learning and Machine Intelligence; Image Analysis and Recognition for Automotive Industry; Adaptive Methods for Ultrasound Beamforming and Motion Estimation.

16th International Conference, ICIAR 2019, Waterloo, ON, Canada, August 27–29, 2019, Proceedings, Part I

Image Analysis and Recognition

16th International Conference, ICIAR 2019, Waterloo, ON, Canada, August 27–29, 2019, Proceedings, Part II

Bart ter Haar Romeny

This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images.

15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings

Fakhri Karray

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