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Supervised learning of local projection kernels Mehmet Gonen , Ethem Alpaydin

Summary: Supervised learning of local projection kernels
Mehmet Go¨nen Ã, Ethem Alpaydin
Department of Computer Engineering, Bogazic-i University, TR-34342 Bebek, _Istanbul, Turkey
a r t i c l e i n f o
Available online 12 March 2010
Dimensionality reduction
Local embedding
Kernel machines
Subspace learning
a b s t r a c t
We formulate a supervised, localized dimensionality reduction method using a gating model that
divides up the input space into regions and selects the dimensionality reduction projection separately
in each region. The gating model, the locally linear projections, and the kernel-based supervised
learning algorithm which uses them in its kernels are coupled and their training is performed with an
alternating optimization procedure. Our proposed local projection kernel projects a data instance into
different feature spaces by using the local projection matrices, combines them with the gating model,
and performs the dot product in the combined feature space. Empirical results on benchmark data sets
for visualization and classification tasks validate the idea. The method is generalizable to regression
estimation and novelty detection.


Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University


Collections: Computer Technologies and Information Sciences