Learning Data Driven Representations from Large Collections of Multidimensional Patterns with Minimal Supervision
Abstract
Traditionally, taking experimental measurements of a physical or biological phenomenon was an expensive, laborious and very slow process. However, significant advances in device technologies and computational techniques have sharply reduced the costs of data collection. Capturing thousands of images of developing biological organisms, or recording enormous amounts of video footage from a network of cameras monitoring an observation space, or obtaining a large number of neural measurements of brain signal patterns via non-invasive devices are some of the examples of such data proliferation. Analyzing such large volumes of multi-dimensional data through expert supervision is neither scalable nor cost-effective. In this context, there is a need for systems that complement the expert user by learning meaningful and compact representations from large collections of multidimensional data (images, videos etc.) with minimal supervision. In this dissertation, we present minimally supervised solutions to two such scenarios generally encountered.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 04, 2008
- Accession Number
- ADA518662
Entities
People
- Parvez Ahammad
Organizations
- University of California, Berkeley