37 research outputs found
On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks
Machine learning (ML) is increasingly applied in optical network management, especially in cross-layer frameworks where physical layer characteristics may trigger changes at the network layer due to transmission performance measurements (quality of transmission, QoT) monitored by optical equipment. Leveraging ML-based QoT estimation approaches has proven to be a promising alternative to exploiting classical mathematical methods or transmission simulation tools. However, supervised ML models rely on large representative training sets, which are often unavailable, due to the lack of the necessary telemetry equipment or of historical data. In such cases, it can be useful to use training data collected from a different network. Unfortunately, the resulting models may be uneffective when applied to the current network, if the training data (the source domain) is not well representative of the network under study (the target domain). Domain adaptation (DA) techniques aim at tackling this issue, to make possible the transfer of knowledge among different networks. This paper compares several DA approaches applied to the problem of estimating the QoT of an optical lightpath using a supervised ML approach. Results show that, when the number of samples from the target domain is limited to a few dozen, DA approaches consistently outperform standard supervised ML techniques
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
Storage Fit Learning with Feature Evolvable Streams
Feature evolvable learning has been widely studied in recent years where old
features will vanish and new features will emerge when learning with streams.
Conventional methods usually assume that a label will be revealed after
prediction at each time step. However, in practice, this assumption may not
hold whereas no label will be given at most time steps. A good solution is to
leverage the technique of manifold regularization to utilize the previous
similar data to assist the refinement of the online model. Nevertheless, this
approach needs to store all previous data which is impossible in learning with
streams that arrive sequentially in large volume. Thus we need a buffer to
store part of them. Considering that different devices may have different
storage budgets, the learning approaches should be flexible subject to the
storage budget limit. In this paper, we propose a new setting: Storage-Fit
Feature-Evolvable streaming Learning (SFEL) which incorporates the issue of
rarely-provided labels into feature evolution. Our framework is able to fit its
behavior to different storage budgets when learning with feature evolvable
streams with unlabeled data. Besides, both theoretical and empirical results
validate that our approach can preserve the merit of the original feature
evolvable learning i.e., can always track the best baseline and thus perform
well at any time step
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis
(SA) aims to leverage useful information in multiple source domains to help do
SA in an unlabeled target domain that has no supervised information. Existing
algorithms of MS-UDA either only exploit the shared features, i.e., the
domain-invariant information, or based on some weak assumption in NLP, e.g.,
smoothness assumption. To avoid these problems, we propose two transfer
learning frameworks based on the multi-source domain adaptation methodology for
SA by combining the source hypotheses to derive a good target hypothesis. The
key feature of the first framework is a novel Weighting Scheme based
Unsupervised Domain Adaptation framework (WS-UDA), which combine the source
classifiers to acquire pseudo labels for target instances directly. While the
second framework is a Two-Stage Training based Unsupervised Domain Adaptation
framework (2ST-UDA), which further exploits these pseudo labels to train a
target private extractor. Importantly, the weights assigned to each source
classifier are based on the relations between target instances and source
domains, which measured by a discriminator through the adversarial training.
Furthermore, through the same discriminator, we also fulfill the separation of
shared features and private features. Experimental results on two SA datasets
demonstrate the promising performance of our frameworks, which outperforms
unsupervised state-of-the-art competitors