Spatio-temporal data mining: A survey of problems and methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …
domains, including climate science, social sciences, neuroscience, epidemiology …
Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
Tensorly: Tensor learning in python
Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone
of traditional machine learning and data analysis, tensor methods have been gaining …
of traditional machine learning and data analysis, tensor methods have been gaining …
Deep learning for distortion prediction in laser-based additive manufacturing using big data
Abstract Laser-Based Additive Manufacturing (LBAM) is a fabrication process that is a key
aspect of Industry 4.0, which aims to employ many sensors for continuous process control …
aspect of Industry 4.0, which aims to employ many sensors for continuous process control …
[图书][B] Sufficient dimension reduction: Methods and applications with R
B Li - 2018 - taylorfrancis.com
Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …
applications in regression diagnostics, data visualization, machine learning, genomics …
[图书][B] Handbook of regression methods
DS Young - 2018 - taylorfrancis.com
Handbook of Regression Methods concisely covers numerous traditional, contemporary,
and nonstandard regression methods. The handbook provides a broad overview of …
and nonstandard regression methods. The handbook provides a broad overview of …
Tensor SVD: Statistical and computational limits
In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …
A survey on tensor techniques and applications in machine learning
This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
Methods for scalar‐on‐function regression
Recent years have seen an explosion of activity in the field of functional data analysis (FDA),
in which curves, spectra, images and so on are considered as basic functional data units. A …
in which curves, spectra, images and so on are considered as basic functional data units. A …