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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Learning, cs.LG
Abstract:
In many learning tasks, the structure of the target space of a function holds
rich information about the relationships between evaluations of functions on
different data points. Existing approaches attempt to exploit this relationship
information implicitly by enforcing smoothness on function evaluations only.
However, what happens if we explicitly regularize the relationships between
function evaluations? Inspired by homophily, we regularize based on a smooth
relationship function, either defined from the data or with labels. In
experiments, we demonstrate that this significantly improves the performance of
state-of-the-art algorithms in semi-supervised classification and in spectral
data embedding for constrained clustering and dimensionality reduction.
