Multimodal And Tensor Data Analytics For Industrial Systems Improvement
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Multimodal And Tensor Data Analytics For Industrial Systems Improvement
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Author : Nathan Gaw
language : en
Publisher: Springer Nature
Release Date : 2024-05-16
Multimodal And Tensor Data Analytics For Industrial Systems Improvement written by Nathan Gaw and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Mathematics categories.
This volume covers the latest methodologies for using multimodal data fusion and analytics across several applications. The curated content presents recent developments and challenges in multimodal data analytics and shines a light on a pathway toward new research developments. Chapters are composed by eminent researchers and practitioners who present their research results and ideas based on their expertise. As data collection instruments have improved in quality and quantity for many applications, there has been an unprecedented increase in the availability of data from multiple sources, known as modalities. Modalities express a large degree of heterogeneity in their form, scale, resolution, and accuracy. Determining how to optimally combine the data for prediction and characterization is becoming increasingly important. Several research studies have investigated integrating multimodality data and discussed the challenges and limitations of multimodal data fusion. This volume provides a topical overview of various methods in multimodal data fusion for industrial engineering and operations research applications, such as manufacturing and healthcare. Advancements in sensing technologies and the shift toward the Internet of Things (IoT) has transformed and will continue to transform data analytics by producing new requirements and more complex forms of data. The abundance of data creates an unprecedented opportunity to design more efficient systems and make near-optimal operational decisions. On the other hand, the structural complexity and heterogeneity of the generated data pose a significant challenge to extracting useful features and patterns for making use of the data and facilitating decision-making. Therefore, continual research is needed to develop new statistical and analytical methodologies that overcome these data challenges and turn them into opportunities.
Tensor Computation For Data Analysis
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Author : Yipeng Liu
language : en
Publisher: Springer Nature
Release Date : 2021-08-31
Tensor Computation For Data Analysis written by Yipeng Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Technology & Engineering categories.
Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
Theory And Algorithms For Reliable Multimodal Data Analysis Machine Learning And Signal Processing
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Author : Dimitris G. Chachlakis
language : en
Publisher:
Release Date : 2021
Theory And Algorithms For Reliable Multimodal Data Analysis Machine Learning And Signal Processing written by Dimitris G. Chachlakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with High performance computing categories.
"Modern engineering systems collect large volumes of data measurements across diverse sensing modalities. These measurements can naturally be arranged in higher-order arrays of scalars which are commonly referred to as tensors. Tucker decomposition (TD) is a standard method for tensor analysis with applications in diverse fields of science and engineering. Despite its success, TD exhibits severe sensitivity against outliers —i.e., heavily corrupted entries that appear sporadically in modern datasets. We study L1-norm TD (L1-TD), a reformulation of TD that promotes robustness. For 3-way tensors, we show, for the first time, that L1-TD admits an exact solution via combinatorial optimization and present algorithms for its solution. We propose two novel algorithmic frameworks for approximating the exact solution to L1-TD, for general N-way tensors. We propose a novel algorithm for dynamic L1-TD —i.e., efficient and joint analysis of streaming tensors. Principal-Component Analysis (PCA) (a special case of TD) is also outlier responsive. We consider Lp-quasinorm PCA (Lp-PCA) for p
Statistically Consistent Support Tensor Machine For Multi Dimensional Data
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Author : Peide Li
language : en
Publisher:
Release Date : 2021
Statistically Consistent Support Tensor Machine For Multi Dimensional Data written by Peide Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic dissertations categories.
Tensors are generalizations of vectors and matrices for multi-dimensional data representation. Fueled by novel computing technologies, tensors have expanded to various domains, including statistics, data science, signal processing, and machine learning. Comparing to traditional data representation formats, tensor data representation distinguishes itself with its capability of preserving complex structures and multi-way features for multi-dimensional data. In this dissertation, we explore some tensor-based classification models and their statistical properties. In particular, we propose few novel support tensor machine methods for huge-size tensor and multimodal tensor classification problems, and study their classification consistency properties. These methods are applied to different applications for validation.The first piece of work considers classification problems for gigantic size multi-dimensional data. Although current tensor-based classification approaches have demonstrated extraordinary performance in empirical studies, they may face more challenges such as long processing time and insufficient computer memory when dealing with big tensors. In chapter 3, we combine tensor-based random projection and support tensor machine, and propose a Tensor Ensemble Classifier(TEC) for ultra-high dimensional tensors, which aggregates multiple support tensor machines estimated from randomly projected CANDECOMP/PARAFAC (CP) tensors. This method utilizes Gaussian and spares random projections to compress high-dimensional tensor CP factors, and predicts their class labels with support tensor machine classifiers. With the well celebrated Johnson-Lindenstrauss Lemma and ensemble techniques, TEC methods are shown to be statistically consistent while having high computational efficiencies for big tensor data. Simulation studies and real data applications including Alzheimer's Disease MRI Image classification and Traffic Image classification are provided as empirical evidence to validate the performance of TEC models.The second piece of work considers classification problems for multimodal tensor data, which are particularly common in neuroscience and brain imaging analysis. Utilizing multimodal data is of great interest for machine learning and statistics research in these domains, since it is believed that integration of features from multiple sources can potentially increase model performance while unveiling the interdependence between heterogeneous data. In chapter 4, we propose a Coupled Support Tensor Machine (C-STM) which adopts Advanced Coupled Matrix Tensor Factorization(ACMTF) and Multiple Kernel Learning (MKL) techniques for coupled matrix tensor data classification. The classification risk of C-STM is shown to be converging to the optimal Bayes risk, making itself a statistically consistent rule. The framework can also be easily extended for multimodal tensors with data modalities greater than two. The C-STM is validated through a simulation study as well as a simultaneous EEG-fMRI trial classification problem. The empirical evidence shows that C-STM can utilize information from multiple sources and provide a better performance comparing to the traditional methods.
User Defined Tensor Data Analysis
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Author : Bin Dong
language : en
Publisher: Springer
Release Date : 2021-10-31
User Defined Tensor Data Analysis written by Bin Dong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-31 with Computers categories.
The SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.