75,910 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
Who Said What: Modeling Individual Labelers Improves Classification
Data are often labeled by many different experts with each expert only
labeling a small fraction of the data and each data point being labeled by
several experts. This reduces the workload on individual experts and also gives
a better estimate of the unobserved ground truth. When experts disagree, the
standard approaches are to treat the majority opinion as the correct label or
to model the correct label as a distribution. These approaches, however, do not
make any use of potentially valuable information about which expert produced
which label. To make use of this extra information, we propose modeling the
experts individually and then learning averaging weights for combining them,
possibly in sample-specific ways. This allows us to give more weight to more
reliable experts and take advantage of the unique strengths of individual
experts at classifying certain types of data. Here we show that our approach
leads to improvements in computer-aided diagnosis of diabetic retinopathy. We
also show that our method performs better than competing algorithms by Welinder
and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative
approach for dealing with the myriad real-world settings that use expert
opinions to define labels for training.Comment: AAAI 201
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