Multimodal data fusion: an overview of methods, challenges, and prospects
In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …
different types of detectors, at different conditions, in multiple experiments or subjects …
Dimension reduction techniques for the integrative analysis of multi-omics data
State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-
throughput 'omics' technologies enable the efficient generation of large experimental data …
throughput 'omics' technologies enable the efficient generation of large experimental data …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
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 …
Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions
Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …
multidimensional data of exceedingly high volume, variety, and structural richness …
Tensor decompositions for signal processing applications: From two-way to multiway component analysis
The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …
highlighted the limitations of standard flat-view matrix models and the necessity to move …
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 …
[HTML][HTML] Tensor decomposition of EEG signals: a brief review
Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG
signals tend to be represented by a vector or a matrix to facilitate data processing and …
signals tend to be represented by a vector or a matrix to facilitate data processing and …
Era of big data processing: A new approach via tensor networks and tensor decompositions
A Cichocki - arXiv preprint arXiv:1403.2048, 2014 - arxiv.org
Many problems in computational neuroscience, neuroinformatics, pattern/image recognition,
signal processing and machine learning generate massive amounts of multidimensional …
signal processing and machine learning generate massive amounts of multidimensional …
Bayesian robust tensor factorization for incomplete multiway data
We propose a generative model for robust tensor factorization in the presence of both
missing data and outliers. The objective is to explicitly infer the underlying low …
missing data and outliers. The objective is to explicitly infer the underlying low …