Add to ORKGSketching is a randomized dimensionalityreduction method that aims to preserve relevant information in large-scale datasets. In this paper, we propose a novel extension known as Higher-order Count Sketch (HCS). We derive efficient (approximate) computation of various tensor operations such as tensor products and tensor contractions directly on the sketched data. HCS is the first sketch to fully exploit the multi-dimensional nature of higher-order tensors
This paper introduces matrix product state (MPS) decomposition as a computational tool for extractin...
With the booming of big data and multi-sensor technology, multi-dimensional data, known as tensors, ...
Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition(SVD), ...
Sketching is a randomized dimensionalityreduction method that aims to preserve relevant information ...
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent ...
Linear algebra is the foundation of machine learning, especially for handling big data. We want to e...
Performing high level cognitive tasks requires the integration of feature maps with drastically diff...
Dimensionality reduction is a fundamental idea in data science and machine learning. Tensor is ubiqu...
Higher-order tensors and their decompositions are abundantly present in domains such as signal proce...
Most visual computing domains are witnessing a steady growth in sheer data set size, complexity, and...
Modern applications in engineering and data science are increasingly based on multidimensional data ...
This paper proposes a novel formulation of the tensor completion problem to impute missing entries o...
Abstract. This survey provides an overview of higher-order tensor decompositions, their applications...
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scal...
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges arou...
This paper introduces matrix product state (MPS) decomposition as a computational tool for extractin...
With the booming of big data and multi-sensor technology, multi-dimensional data, known as tensors, ...
Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition(SVD), ...
Sketching is a randomized dimensionalityreduction method that aims to preserve relevant information ...
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent ...
Linear algebra is the foundation of machine learning, especially for handling big data. We want to e...
Performing high level cognitive tasks requires the integration of feature maps with drastically diff...
Dimensionality reduction is a fundamental idea in data science and machine learning. Tensor is ubiqu...
Higher-order tensors and their decompositions are abundantly present in domains such as signal proce...
Most visual computing domains are witnessing a steady growth in sheer data set size, complexity, and...
Modern applications in engineering and data science are increasingly based on multidimensional data ...
This paper proposes a novel formulation of the tensor completion problem to impute missing entries o...
Abstract. This survey provides an overview of higher-order tensor decompositions, their applications...
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scal...
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges arou...
This paper introduces matrix product state (MPS) decomposition as a computational tool for extractin...
With the booming of big data and multi-sensor technology, multi-dimensional data, known as tensors, ...
Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition(SVD), ...