Deep learning with application to hashing
Date of Issue2014
School of Computer Engineering
Deep Learning and Learning to Hash are two important research areas in machine learning, which have rapid improvements in recent years. What I mainly researched on is an inter-discipline field: deep learning for cross view hashing. Multiple layers of representation in deep learning has the property of abstracting representation from input data, while, in the cross view similarity search, the biggest difficulty is to represent items from one domain to another. Here, I want to take advantage of the latest deep learning technology to solve the cross view similarity search problem. Hashing is used to accelerate this process. This thesis mainly contains three parts. Chapter 2 is a literature survey. It contains a deep learning survey and a learning to hash survey. The deep learning survey briefly introduces fundamental technology of deep learning and its recent development including the latest technology. The Learning to Hash survey brief introduces some widely used learning to hash algorithms. Chapter 3 is an experiment about comparison of some state of the arts learning to hash algorithms. Chapter 4 is cross view hashing based on deep learning. I present a cross view feature hashing technique using deep learning and show some results. These three chapters are main chapters. Chapter 1 and Chapter 5 are introduction and conclusion.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence