Abstract: Since the impressive advances in the area of compressed
sensing, dimensionality reduction by random projections for machine
learning and data mining gains a renewed interest. In direct analogy,
compressive learning means to carry out learning tasks efficiently on
cheaply compressed versions of high dimensional massive data sets that
have a sparse representation. This talk will discuss conditions and
guarantees for compressive learning to succeed, which do not require
the data to have a sparse representation but instead exploit the
natural structure of the learning problem. In particular, we give
tight risk bounds in classification and regression settings, which
have a clear interpretation and reveal meaningful structural
properties of the problem that make it solvable effectively in a small
dimensional random subspace. We will also demonstrate that performance
gains are achievable by combining several compressive learners into an
ensemble. Speaker Bio: Dr. Ata Kaban is currently senior lecturer in Computer Science at the
University of Birmingham. She recieved her PhD in Computer Science from the
University of Paisley, supervised by Mark Girolami. She also holds a PhD in
Musicology. Her research interests are: statistical machine learning, data
mining - with emphasis on high dimensional data spaces; algorithmic learning
theory; probabilistic modelling of data, and Bayesian inference; high
dimensional phenomena, measure concentration, random matrix theory;
dimensionality reduction, random projections; large-scale heuristic
black-box optimisation. Speaker's Webpage: http://www.cs.bham.ac.uk/~axk/ Slides for the talk: PDF Video of the talk here. |