14,470 research outputs found
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via
information (e.g., labels of an item) provided by labelers. Current
crowdsourcing algorithms are mainly unsupervised methods that are unaware of
the quality of crowdsourced data. In this paper, we propose a supervised
collective classification algorithm that aims to identify reliable labelers
from the training data (e.g., items with known labels). The reliability (i.e.,
weighting factor) of each labeler is determined via a saddle point algorithm.
The results on several crowdsourced data show that supervised methods can
achieve better classification accuracy than unsupervised methods, and our
proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM)
Workshop on Networking and Collaboration Issues for the Internet of
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Semi-Supervised Sparse Coding
Sparse coding approximates the data sample as a sparse linear combination of
some basic codewords and uses the sparse codes as new presentations. In this
paper, we investigate learning discriminative sparse codes by sparse coding in
a semi-supervised manner, where only a few training samples are labeled. By
using the manifold structure spanned by the data set of both labeled and
unlabeled samples and the constraints provided by the labels of the labeled
samples, we learn the variable class labels for all the samples. Furthermore,
to improve the discriminative ability of the learned sparse codes, we assume
that the class labels could be predicted from the sparse codes directly using a
linear classifier. By solving the codebook, sparse codes, class labels and
classifier parameters simultaneously in a unified objective function, we
develop a semi-supervised sparse coding algorithm. Experiments on two
real-world pattern recognition problems demonstrate the advantage of the
proposed methods over supervised sparse coding methods on partially labeled
data sets
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains
There has been increased interest in devising learning techniques that
combine unlabeled data with labeled data ? i.e. semi-supervised learning.
However, to the best of our knowledge, no study has been performed across
various techniques and different types and amounts of labeled and unlabeled
data. Moreover, most of the published work on semi-supervised learning
techniques assumes that the labeled and unlabeled data come from the same
distribution. It is possible for the labeling process to be associated with a
selection bias such that the distributions of data points in the labeled and
unlabeled sets are different. Not correcting for such bias can result in biased
function approximation with potentially poor performance. In this paper, we
present an empirical study of various semi-supervised learning techniques on a
variety of datasets. We attempt to answer various questions such as the effect
of independence or relevance amongst features, the effect of the size of the
labeled and unlabeled sets and the effect of noise. We also investigate the
impact of sample-selection bias on the semi-supervised learning techniques
under study and implement a bivariate probit technique particularly designed to
correct for such bias
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
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