IJCNN 2019

Table of Contents generated with DocToc

Last Update: 2019-07-14

• 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
  • Removing venv and ipython kernel
• 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/ijcnn-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

Note: Regardless, of your operating system, make sure that you have Anaconda

cd ijcnn-2019

# Create venv with conda
conda create -y --name "ijcnn-2019" python=3.6.5 pip
conda activate "ijcnn-2019"

# Install dependencies for this tutorial
pip  install -r requirements.txt    

# Install kernel if you prefer to
python -m ipykernel install --user --name "ijcnn-2019" --display-name "ijcnn-2019"

# Install jupyterlab extensions (for interactive visualisations)
jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
jupyter labextension install @jupyterlab/toc --no-build
jupyter lab clean
jupyter lab build

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

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

cd ijcnn-2019
./boostrap-venv.sh

If during setup process you get error message

RemoveError: 'requests' is a dependency of conda and cannot be removed from canada's operating environment.

then you need to update your conda package and and cleanup location where conda installes virtual environments

conda update conda
rm -rf ${ANACONDA_HOME}/envs/ijcnn-2019

Typically, ${ANACONDA_HOME} resides in the root of your home directory

Start Jupyter Lab

cd ijcnn-2019
conda activate ijcnn-2019
jupyter lab

Removing venv and ipython kernel

conda deactivate
jupyter kernelspec uninstall ijcnn-2019
conda env remove -n ijcnn-2019

Supplementary materials

• HOTTBOX tutorials and documentation

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.