A seminar of the A3SI team of the LIGM (mixed research unit of the University Paris Est) will take place on Thursday May 17 from 1:30 pm to 2:30 pm at ESIEE Paris (room 210).
Abstract: Representation learning techniques have gained popularity over the years. Machine Learning community is well aware of several representation learning tools, viz. AutoEncoders, Deep belief networks, Convolutional Neural Networks and Dictionary Learning(similar to matrix factorization and latent factor model). While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, Transform Learning is a new form of representation learning. It is more generalized analysis equivalent of dictionary learning. Till now, Transform Learning has been restricted to the signal processing community.
We start with a standard algebraic model and keep converting our intuitions and observations into mathematical model. We develop formulations aimed at learning representations from data. In this talk, the major part will cover the importance of transform learning and its advantages over other representation learning techniques. Then, Supervised Transform Learning and Deep Transform Learning will be discussed, followed by one practical application..