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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Low Rank and Sparse Modeling for Visual Analysis

multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data;

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Advanced Sparsity-Driven Models and Methods for Radar Applications

-disk sampling for high-resolution and wide-swath SAR imaging; when advanced sparse models meet coarsely quantized radar data; sparsity-aware;

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High-dimensional Data Analysis

Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide;

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High-Dimensional Covariance Estimation

. High-Dimensional Covariance Estimation features chapters on: * Data, Sparsity, and Regularization * Regularizing the Eigenstructure * Banding;

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Sparse Grids and Applications Miami 2016

Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes;

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Feature Selection for High-Dimensional Data

foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis;

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Data Science

. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and;

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Perspectives on Big Data Analysis

estimation for an array of interesting models. The book also presents some surprising results on high-dimensional data analysis, especially when;

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Robust Representation for Data Analytics

data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection;

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Sparse Optimization Theory and Methods

processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and;

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Sparse Optimization Theory and Methods

processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and;

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Understanding High-Dimensional Spaces

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space;

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Curves and Surfaces

processing and learning theory, sparse and high-dimensional approximation, subdivision, wavelets and multi-resolution method.;

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Intelligent Decision Technologies

applications Multi-criteria decision analysis methods Knowledge engineering in large-scale systems High-dimensional data analysis Spatial data;

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High-Dimensional Statistics

introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core;

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Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference

This book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. It specifically considers;

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Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance

This book deals with the numerical analysis and efficient numerical treatment of high-dimensional integrals using sparse grids and other;

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Statistical Methods for Recommender Systems

challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling;

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Sufficient Dimension Reduction for the Conditional Kth Moment

preserving all the relevant information for either regression or classification and effectively transforms high dimensional problem to lower;

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High Dimensional Econometrics and Identification

recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the;

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fastText Quick Start Guide

fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable;

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Model-Based Clustering and Classification for Data Science

approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering;

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Sparse Coding and Its Applications in Computer Vision

introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research;

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Deep Learning through Sparse and Low-Rank Modeling

variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for;

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Financial Signal Processing and Machine Learning

processing and machine learning as key approaches to quantitative finance. * Offers advanced mathematical tools for high-dimensional portfolio;

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Statistical Learning with Sparsity

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters;

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