Manifold Learning for Anamoly Detection

Modern data analysis tasks often face challenges of high dimension and thus nonlinear dimension reduction techniques emerge as a way to construct maps from high dimensional data to their corresponding low dimensional representations. Finding such low dimensional representations of high dimensional data is beneficial in several aspects. This saves space and processing time. More importantly, the low dimensional representation often provides a better understanding of the intrinsic structure of data, which often leads to better features that can be fed into further data analysis algorithms. Manifold learning is a subfield of machine learning that focuses on dimensionality reduction, with the goal of capturing the underlying structure of high-dimensional data in lower-dimensional representations. The concept behind manifold learning is that even though data might live in a high-dimensional space, it might be constrained to a lower-dimensional manifold within that space. Methods like t-SNE, Isomap, and locally linear embedding (LLE) are some popular techniques in this category. Our aim of this internship project will be to apply manifold learning on tabular data and transactional graph data and see how it can help in better fraud detection (anamoly), feature engineering, clustering, etc.

Faculty Supervisor:

Rachid Hedjam

Student:

Partner:

Mastercard

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Bishop's University

Program: