Manifold Learning and Dimensionality Reduction for Data Visualization and Feature Engineering

Dimensionality Reduction is not only useful for de-noising purposes or making data better accessible, it is also very important for Exploratory Data Analysis, especially with respect to Data Visualization. Manifold Learning subsumes a collection of advanced methods from the field of Unsupervised Learning that capture different aspects of the given high-dimensional data in a low-dimensional manifold. Each method tries to preserve an important quantity – distances between points, variance, statistical or distributional properties. The variety of these methods offers some new and interesting options for Feature Engineering and the ultimate task of Machine Learning and AI – “Learning from Data”.

Required audience experience

Familiarity with basic Machine Learning concepts

Objective of the talk

• Get a solid understanding of the power and limits of the various Dimensionality Reduction and Manifold Learning methods from Random Projections to Principal Component Analysis to Multidimensional Scaling etc. to t-SNE
• Make use of these methods for Data Vizualisation and Feature Engineering

You can view Stefan’s slides and presentation below:

Stefan Kühn MCubed presentation