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
You can view Stefan’s slides and presentation below: