Abstract: In this paper, we introduce a generic framework
for semi-supervised kernel learning. Given pairwise
(dis-)similarity constraints, we learn a kernel
matrix over the data that respects the provided
side-information as well as the local geometry
of the data. Our framework is based
on metric learning methods, where we jointly
model the metric/kernel over the data along with
the underlying manifold. Furthermore, we show
that for some important parameterized forms of
the underlying manifold model, we can estimate
the model parameters and the kernel matrix efficiently.
Our resulting algorithm is able to incorporate
local geometry into the metric learning
task; at the same time it can handle a wide class
of constraints. Finally, our algorithm is fast and
scalable – unlike most of the existing methods, it
is able to exploit the low dimensional manifold
structure and does not require semi-definite programming.
We demonstrate wide applicability
and effectiveness of our framework by applying
to various machine learning tasks such as semisupervised
classification, colored dimensionality
reduction, manifold alignment etc. On each of
the tasks our method performs competitively or
better than the respective state-of-the-art method.
- Topics:
- Metric Learning
Download: pdf
Citation
- Geometry-aware Metric Learning (pdf, software)
Z. Lu, P. Jain, I. Dhillon.
In International Conference on Machine Learning (ICML), June 2009.
Bibtex: