Semi-Supervised, Dynamic Class-Informative Feature Learning

• Activity Number: 294 - SPEED: Statistical Learning and Data Science Speed Session 2, Part 1
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Vincent Pisztora*
• Keywords: Dimension Reduction; Representation Learning; Semi-Supervised; Neural Networks; Deep Learning

Abstract

All successful classification tasks depend critically on a representation of the data for which there exists a learnable function distinguishing the classes. Without such a “class-informative” set of features, classification is not possible. A methodology is proposed which provides non-linear, semi-supervised class-informative feature set learning using a novel loss function and a dynamic training scheme. In the semi-supervised case, this methodology is shown to improve classification performance by incorporating the structure of unlabeled observations into the learned feature map.