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Transductive Ordinal Regression
Ordinal regression is commonly formulated as a multi-class problem with
ordinal constraints. The challenge of designing accurate classifiers for
ordinal regression generally increases with the number of classes involved, due
to the large number of labeled patterns that are needed. The availability of
ordinal class labels, however, is often costly to calibrate or difficult to
obtain. Unlabeled patterns, on the other hand, often exist in much greater
abundance and are freely available. To take benefits from the abundance of
unlabeled patterns, we present a novel transductive learning paradigm for
ordinal regression in this paper, namely Transductive Ordinal Regression (TOR).
The key challenge of the present study lies in the precise estimation of both
the ordinal class label of the unlabeled data and the decision functions of the
ordinal classes, simultaneously. The core elements of the proposed TOR include
an objective function that caters to several commonly used loss functions
casted in transductive settings, for general ordinal regression. A label
swapping scheme that facilitates a strictly monotonic decrease in the objective
function value is also introduced. Extensive numerical studies on commonly used
benchmark datasets including the real world sentiment prediction problem are
then presented to showcase the characteristics and efficacies of the proposed
transductive ordinal regression. Further, comparisons to recent
state-of-the-art ordinal regression methods demonstrate the introduced
transductive learning paradigm for ordinal regression led to the robust and
improved performance
Transductive Multi-View Zero-Shot Learning
(c) 2012. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms
Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
Distribution matching for transduction
Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.
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