Abstract:
Many problems in machine learning involve nding interesting non-linear
relationships in high dimensional data. Kernel methods in statistical learning
theory provide powerful techniques for analyzing such data. They can be used
for both supervised and non-supervised learning problems like classi cation
using support vector machines and kernel spectral clustering respectively.
The basic idea behind kernel methods is to map/embed the data nonlinearly
to a higher dimensional feature space, where linear algorithms
are applied. Recent advances in kernel based methods allow embedding
probability distribution, conditional and posterior distribution to the higher
dimensional feature space. This thesis proposes a non-parametric method
to robustly embed the conditional and posterior distribution in reproducing
Kernel Hilbert Space (RKHS). Robust embedding is achieved by eigenvalue
decomposition in RKHS and by retaining only the leading eigenvectors. The
robust posterior distribution achieved using our approach can be applied
to wide range of Bayesian inference problems. In this thesis, we apply it
for recognizing food dishes from images, when no training images of target
food dishes are given. Algorithms that aim to recognize objects for which
we have no training examples (unseen object classes), are called zero-shot
learning (ZSL) algorithms. Image features and attribute representations of
food dishes are gathered using deep convolutional network and word vector
space embedding of large web corpora respectively. Our approach try to
learn the relation among image features and attributes which can further be
used to classify food dishes given only attributes.
