22,138 research outputs found
Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection
The utilization of computer technology to solve problems in medical scenarios
has attracted considerable attention in recent years, which still has great
potential and space for exploration. Among them, machine learning has been
widely used in the prediction, diagnosis and even treatment of Sepsis. However,
state-of-the-art methods require large amounts of labeled medical data for
supervised learning. In real-world applications, the lack of labeled data will
cause enormous obstacles if one hospital wants to deploy a new Sepsis detection
system. Different from the supervised learning setting, we need to use known
information (e.g., from another hospital with rich labeled data) to help build
a model with acceptable performance, i.e., transfer learning. In this paper, we
propose a semi-supervised optimal transport with self-paced ensemble framework
for Sepsis early detection, called SPSSOT, to transfer knowledge from the other
that has rich labeled data. In SPSSOT, we first extract the same clinical
indicators from the source domain (e.g., hospital with rich labeled data) and
the target domain (e.g., hospital with little labeled data), then we combine
the semi-supervised domain adaptation based on optimal transport theory with
self-paced under-sampling to avoid a negative transfer possibly caused by
covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end
transfer learning method for Sepsis early detection which can automatically
select suitable samples from two domains respectively according to the number
of iterations and align feature space of two domains. Extensive experiments on
two open clinical datasets demonstrate that comparing with other methods, our
proposed SPSSOT, can significantly improve the AUC values with only 1% labeled
data in the target domain in two transfer learning scenarios, MIMIC
Challenge and Challenge MIMIC.Comment: 14 pages, 9 figure
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
In semi-supervised domain adaptation (SSDA), a few labeled target samples of
each class help the model to transfer knowledge representation from the fully
labeled source domain to the target domain. Many existing methods ignore the
benefits of making full use of the labeled target samples from multi-level. To
make better use of this additional data, we propose a novel Prototype-based
Multi-level Learning (ProML) framework to better tap the potential of labeled
target samples. To achieve intra-domain adaptation, we first introduce a
pseudo-label aggregation based on the intra-domain optimal transport to help
the model align the feature distribution of unlabeled target samples and the
prototype. At the inter-domain level, we propose a cross-domain alignment loss
to help the model use the target prototype for cross-domain knowledge transfer.
We further propose a dual consistency based on prototype similarity and linear
classifier to promote discriminative learning of compact target feature
representation at the batch level. Extensive experiments on three datasets,
including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed
method achieves state-of-the-art performance in SSDA.Comment: IJCAI 202
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
Kernel Manifold Alignment
We introduce a kernel method for manifold alignment (KEMA) and domain
adaptation that can match an arbitrary number of data sources without needing
corresponding pairs, just few labeled examples in all domains. KEMA has
interesting properties: 1) it generalizes other manifold alignment methods, 2)
it can align manifolds of very different complexities, performing a sort of
manifold unfolding plus alignment, 3) it can define a domain-specific metric to
cope with multimodal specificities, 4) it can align data spaces of different
dimensionality, 5) it is robust to strong nonlinear feature deformations, and
6) it is closed-form invertible which allows transfer across-domains and data
synthesis. We also present a reduced-rank version for computational efficiency
and discuss the generalization performance of KEMA under Rademacher principles
of stability. KEMA exhibits very good performance over competing methods in
synthetic examples, visual object recognition and recognition of facial
expressions tasks
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linear transformation between the joint feature/label space distributions
of the two domain and . We propose a solution of
this problem with optimal transport, that allows to recover an estimated target
by optimizing simultaneously the optimal coupling
and . We show that our method corresponds to the minimization of a bound on
the target error, and provide an efficient algorithmic solution, for which
convergence is proved. The versatility of our approach, both in terms of class
of hypothesis or loss functions is demonstrated with real world classification
and regression problems, for which we reach or surpass state-of-the-art
results.Comment: Accepted for publication at NIPS 201
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