2,518 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
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
Classification of geometrical objects by integrating currents and functional data analysis. An application to a 3D database of Spanish child population
This paper focuses on the application of Discriminant Analysis to a set of
geometrical objects (bodies) characterized by currents. A current is a relevant
mathematical object to model geometrical data, like hypersurfaces, through
integration of vector fields along them. As a consequence of the choice of a
vector-valued Reproducing Kernel Hilbert Space (RKHS) as a test space to
integrate hypersurfaces, it is possible to consider that hypersurfaces are
embedded in this Hilbert space. This embedding enables us to consider
classification algorithms of geometrical objects. A method to apply Functional
Discriminant Analysis in the obtained vector-valued RKHS is given. This method
is based on the eigenfunction decomposition of the kernel. So, the novelty of
this paper is the reformulation of a size and shape classification problem in
Functional Data Analysis terms using the theory of currents and vector-valued
RKHS. This approach is applied to a 3D database obtained from an anthropometric
survey of the Spanish child population with a potential application to online
sales of children's wear
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