ilyakisil/inns-2019

:mortar_board: INNS BIG DATA AND DEEP LEARNING 16-18 April 2019 || Materials for the tutorial "Tensor Decompositions and Applications. Blessing of Dimensionality"

INNS 2019

Table of Contents generated with DocToc

Last Update: 2019-04-16

• I want to follow along in a Cloud
• I want to follow along on my PC
  • Getting source files
  • Preparing working environment
  • Start Jupyter Lab
• Supplementary materials
• Literature references
• Reporting problems and issues

I want to follow along in a Cloud

• This is as simple as clicking on the binder badge above
• No headache with installation and/or configuration
• Requires internet connection
• Fresh environment when binder session expires

Although, this option comes at the cost of lower computational resources being available to you, but it will be sufficient for the introductory purpose of this tutorial.

Note: It may take a couple of minutes to launch a binder server. If it takes longer then that, try to refresh the web page before reporting this issue.

I want to follow along on my PC

Getting source files

Preferred option is to clone this repository using git.

git clone https://github.com/IlyaKisil/inns-2019.git

Alternatively, you can download a ZIP folder with all materials for this assignment by using the Clone or Download button (in green color) at the top of this page.

Preparing working environment

If you are on Unix, then simply execute in terminal:

cd inns-2019
./boostrap-venv.sh

If you are on Windows, then you can replicate bootstrap-ven.sh with the following steps:

1. Open Anaconda prompt and execute:

   cd inns-2019
   conda create -y --name "inns-2019" python=3.6.5
   conda activate "inns-2019"
   pip  install -r requirements.txt    
   python -m ipykernel install --user --name "inns-2019" --display-name "inns-2019"

2. Then download this dataset and extract it into the data directory.

Note: Regardless, of your operating system, make sure that you have Anaconda and Jupyter Lab installed.

Start Jupyter Lab

cd inns-2019
jupyter lab

Supplementary materials

• HOTTBOX tutorials and documentation
• Slides for this tutorial talk
• Home page of this project

Literature references

• Kolda, Tamara G., et al. "Tensor decompositions and applications." SIAM review 51.3 (2009): 455-500.
• Cichocki, Andrzej, et al. "Tensor decompositions for signal processing applications: From two-way to multiway component analysis." IEEE Signal Processing Magazine 32.2 (2015): 145-163.
• Cichocki, Andrzej, et al. "Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions." Foundations and Trends® in Machine Learning 9.4-5 (2016): 249-429.
• De Lathauwer, Lieven, et al. "A multilinear singular value decomposition." SIAM journal on Matrix Analysis and Applications 21.4 (2000): 1253-1278.
• Fanaee-T, Hadi, et al. "Tensor-based anomaly detection: An interdisciplinary survey." Knowledge-Based Systems 98 (2016): 130-147.
• Kisil, Ilia, et al. "Tensor ensemble learning for multidimensional data." 2018 IEEE Global Conference on Signal and Information Processing (2018): 1358-1362.

Reporting problems and issues

Please use one of these forms which supports markdown text formatting. It would also be helpful if you include as much relevant information as possible. This could include screenshots, code snippets etc.