A literature survey of low‐rank tensor approximation techniques
During the last years, low‐rank tensor approximation has been established as a new tool in
scientific computing to address large‐scale linear and multilinear algebra problems, which …
scientific computing to address large‐scale linear and multilinear algebra problems, which …
Tensors for data mining and data fusion: Models, applications, and scalable algorithms
Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
Low-rank tucker decomposition of large tensors using tensorsketch
We propose two randomized algorithms for low-rank Tucker decomposition of tensors. The
algorithms, which incorporate sketching, only require a single pass of the input tensor and …
algorithms, which incorporate sketching, only require a single pass of the input tensor and …
Parcube: Sparse parallelizable tensor decompositions
How can we efficiently decompose a tensor into sparse factors, when the data does not fit in
memory? Tensor decompositions have gained a steadily increasing popularity in data …
memory? Tensor decompositions have gained a steadily increasing popularity in data …
Low-rank tucker approximation of a tensor from streaming data
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a
tensor. The method applies a randomized linear map to the tensor to obtain a sketch that …
tensor. The method applies a randomized linear map to the tensor to obtain a sketch that …
Colorful triangle counting and a mapreduce implementation
In this note we introduce a new randomized algorithm for counting triangles in graphs. We
show that under mild conditions, the estimate of our algorithm is strongly concentrated …
show that under mild conditions, the estimate of our algorithm is strongly concentrated …
Fast and guaranteed tensor decomposition via sketching
Abstract Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in
statistical learning of latent variable models and in data mining. In this paper, we propose …
statistical learning of latent variable models and in data mining. In this paper, we propose …
Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
Randomized algorithms for low-rank tensor decompositions in the Tucker format
Many applications in data science and scientific computing involve large-scale datasets that
are expensive to store and manipulate. However, these datasets possess inherent …
are expensive to store and manipulate. However, these datasets possess inherent …
Multiaspectforensics: Pattern mining on large-scale heterogeneous networks with tensor analysis
Modern applications such as web knowledge base, network traffic monitoring and online
social networks have made available an unprecedented amount of network data with rich …
social networks have made available an unprecedented amount of network data with rich …