80 research outputs found
Neural Class-Specific Regression for face verification
Face verification is a problem approached in the literature mainly using
nonlinear class-specific subspace learning techniques. While it has been shown
that kernel-based Class-Specific Discriminant Analysis is able to provide
excellent performance in small- and medium-scale face verification problems,
its application in today's large-scale problems is difficult due to its
training space and computational requirements. In this paper, generalizing our
previous work on kernel-based class-specific discriminant analysis, we show
that class-specific subspace learning can be cast as a regression problem. This
allows us to derive linear, (reduced) kernel and neural network-based
class-specific discriminant analysis methods using efficient batch and/or
iterative training schemes, suited for large-scale learning problems. We test
the performance of these methods in two datasets describing medium- and
large-scale face verification problems.Comment: 9 pages, 4 figure
Incremental Principal Component Analysis Exact implementation and continuity corrections
This paper describes some applications of an incremental implementation of
the principal component analysis (PCA). The algorithm updates the
transformation coefficients matrix on-line for each new sample, without the
need to keep all the samples in memory. The algorithm is formally equivalent to
the usual batch version, in the sense that given a sample set the
transformation coefficients at the end of the process are the same. The
implications of applying the PCA in real time are discussed with the help of
data analysis examples. In particular we focus on the problem of the continuity
of the PCs during an on-line analysis.Comment: accepted at http://www.icinco.org
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