13 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
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
Graph Embedding with Data Uncertainty
spectral-based subspace learning is a common data preprocessing step in many
machine learning pipelines. The main aim is to learn a meaningful low
dimensional embedding of the data. However, most subspace learning methods do
not take into consideration possible measurement inaccuracies or artifacts that
can lead to data with high uncertainty. Thus, learning directly from raw data
can be misleading and can negatively impact the accuracy. In this paper, we
propose to model artifacts in training data using probability distributions;
each data point is represented by a Gaussian distribution centered at the
original data point and having a variance modeling its uncertainty. We
reformulate the Graph Embedding framework to make it suitable for learning from
distributions and we study as special cases the Linear Discriminant Analysis
and the Marginal Fisher Analysis techniques. Furthermore, we propose two
schemes for modeling data uncertainty based on pair-wise distances in an
unsupervised and a supervised contexts.Comment: 20 pages, 4 figure