12,787 research outputs found
Joint cross-domain classification and subspace learning for unsupervised adaptation
Domain adaptation aims at adapting the knowledge acquired on a source domain
to a new different but related target domain. Several approaches have
beenproposed for classification tasks in the unsupervised scenario, where no
labeled target data are available. Most of the attention has been dedicated to
searching a new domain-invariant representation, leaving the definition of the
prediction function to a second stage. Here we propose to learn both jointly.
Specifically we learn the source subspace that best matches the target subspace
while at the same time minimizing a regularized misclassification loss. We
provide an alternating optimization technique based on stochastic sub-gradient
descent to solve the learning problem and we demonstrate its performance on
several domain adaptation tasks.Comment: Paper is under consideration at Pattern Recognition Letter
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
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