15,699 research outputs found
AutoEncoder Inspired Unsupervised Feature Selection
High-dimensional data in many areas such as computer vision and machine
learning tasks brings in computational and analytical difficulty. Feature
selection which selects a subset from observed features is a widely used
approach for improving performance and effectiveness of machine learning models
with high-dimensional data. In this paper, we propose a novel AutoEncoder
Feature Selector (AEFS) for unsupervised feature selection which combines
autoencoder regression and group lasso tasks. Compared to traditional feature
selection methods, AEFS can select the most important features by excavating
both linear and nonlinear information among features, which is more flexible
than the conventional self-representation method for unsupervised feature
selection with only linear assumptions. Experimental results on benchmark
dataset show that the proposed method is superior to the state-of-the-art
method.Comment: accepted by ICASSP 201
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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