• Corpus ID: 244800775

HOTTBOX: Higher Order Tensor ToolBOX

  title={HOTTBOX: Higher Order Tensor ToolBOX},
  author={Ilya Kisil and Giuseppe Giovanni Calvi and Bruno Scalzo Dees and Danilo P. Mandic},
HOTTBOX is a Python library for exploratory analysis and visualisation of multi-dimensional arrays of data, also known as tensors. The library includes methods ranging from standard multi-way operations and data manipulation through to multi-linear algebra based tensor decompositions. HOTTBOX also comprises sophisticated algorithms for generalised multi-linear classification and data fusion, such as Support Tensor Machine (STM) and Tensor Ensemble Learning (TEL). For user convenience, HOTTBOX… 

Figures from this paper

Tensor Decompositions in Deep Learning
The topic of tensor decompositions in modern machine learning applications is surveyed and how tensor methods can be leveraged to yield richer adaptive representations of complex data, including structured information is discussed.


A novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced by virtue of tensor decompositions, which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance.
Applications of tensor (multiway array) factorizations and decompositions in data mining
  • M. Mørup
  • Computer Science
    WIREs Data Mining Knowl. Discov.
  • 2011
The aim of this overview is to introduce the basic concepts of tensor decompositions and demonstrate some of the many benefits and challenges of modeling data multiway for a wide variety of data and problem domains.
TensorLy: Tensor Learning in Python
TensorLy is a Python library that provides a high-level API for tensor methods and deep tensorized neural networks and aims to follow the same standards adopted by the main projects of the Python scientific community, and to seamlessly integrate with them.
SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries
This paper proposes SCOUT, a large-scale coupled matrix-tensor factorization algorithm running on the distributed MAPREDUCE platform that decomposes up to 100× larger tensors than existing methods, and shows linear scalability for order and machines while other methods are limited in scalability.
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives
This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics.
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
A focus is on the Tucker and tensor train TT decompositions and their extensions, and on demonstrating the ability of tensor network to provide linearly or even super-linearly e.g., logarithmically scalablesolutions, as illustrated in detail in Part 2 of this monograph.
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.
Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data
It is shown how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data.
Tensor Decompositions and Applications
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order
Tensor Train decomposition on TensorFlow (T3F)
A library that aims to fix Tensor Train decomposition and makes machine learning papers that rely on Tensor train decomposition easier to implement and includes 92% test coverage, examples, and API reference documentation.