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PKDD
2010
Springer

Laplacian Spectrum Learning

8 years 12 months ago
Laplacian Spectrum Learning
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplacian to obtain a kernel. While manual exploration of the spectrum is conceivable, non-parametric learning methods that adjust the Laplacian’s spectrum promise better performance. For instance, adjusting the graph Laplacian using kernel target alignment (KTA) yields better performance when an SVM is trained on the resulting kernel. KTA relies on a simple surrogate criterion to choose the kernel; the obtained kernel is then fed to a large margin classification algorithm. In this paper, we propose novel formulations that jointly optimize relative margin and the spectrum of a kernel defined via Laplacian eigenmaps. The large relative margin case is in fact a strict generalization of the large margin case. The proposed methods show significant empirical advantage over numerous other competing methods.
Pannagadatta K. Shivaswamy, Tony Jebara
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PKDD
Authors Pannagadatta K. Shivaswamy, Tony Jebara
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