Implement the CONCORD algorithm into the HPCC Systems® Machine Learning Library
This project was completed by Syed Rahman. The project was his own idea which he brought to us and completed as a summer intern in 2015.
The CONCORD algorithm implemented by Syed Rahman
The CONCORD algorithm is a method to estimate the true population of a co-variance matrix. The co-variance matrix is a summary of the relationship between every pair of fields in the data. Co-variance values close to zero indicate that the fields don’t have a relationship. Values close to 1 indicate a positive relationship and values close to –1 indicate an inverse relationship.
For further details please refer to the following JIRA issue for this project.
In 2016, Syed was a returning student intern who completed a machine learning project which is related to this algorithm.
- Find out more about his second project to implement the Convex Sparse Cholesky Selection (CSCS) machine learning algorithm.
- View Syed's technical poster presentation on the CSCS algorithm displayed on Community Day at the HPCC Systems Engineering Summit in 2016 where he was a 3rd place winner.
- Watch a recording of his presentation on Understanding High-dimensional Networks for Continuous Variables Using ECL, on Community Day at the HPCC Systems Engineering Summit in 2016 or view the presentation slides.