5,787 research outputs found
Deep Multi-view Learning to Rank
We study the problem of learning to rank from multiple information sources.
Though multi-view learning and learning to rank have been studied extensively
leading to a wide range of applications, multi-view learning to rank as a
synergy of both topics has received little attention. The aim of the paper is
to propose a composite ranking method while keeping a close correlation with
the individual rankings simultaneously. We present a generic framework for
multi-view subspace learning to rank (MvSL2R), and two novel solutions are
introduced under the framework. The first solution captures information of
feature mappings from within each view as well as across views using
autoencoder-like networks. Novel feature embedding methods are formulated in
the optimization of multi-view unsupervised and discriminant autoencoders.
Moreover, we introduce an end-to-end solution to learning towards both the
joint ranking objective and the individual rankings. The proposed solution
enhances the joint ranking with minimum view-specific ranking loss, so that it
can achieve the maximum global view agreements in a single optimization
process. The proposed method is evaluated on three different ranking problems,
i.e. university ranking, multi-view lingual text ranking and image data
ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
Hyper-spectral data can be analyzed to recover physical properties at large
planetary scales. This involves resolving inverse problems which can be
addressed within machine learning, with the advantage that, once a relationship
between physical parameters and spectra has been established in a data-driven
fashion, the learned relationship can be used to estimate physical parameters
for new hyper-spectral observations. Within this framework, we propose a
spatially-constrained and partially-latent regression method which maps
high-dimensional inputs (hyper-spectral images) onto low-dimensional responses
(physical parameters such as the local chemical composition of the soil). The
proposed regression model comprises two key features. Firstly, it combines a
Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent
response model. While the former makes high-dimensional regression tractable,
the latter enables to deal with physical parameters that cannot be observed or,
more generally, with data contaminated by experimental artifacts that cannot be
explained with noise models. Secondly, spatial constraints are introduced in
the model through a Markov random field (MRF) prior which provides a spatial
structure to the Gaussian-mixture hidden variables. Experiments conducted on a
database composed of remotely sensed observations collected from the Mars
planet by the Mars Express orbiter demonstrate the effectiveness of the
proposed model.Comment: 12 pages, 4 figures, 3 table
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