227,048 research outputs found
Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum
This paper presents a spectral attention-driven reinforcement learning based
intelligent method for effective and efficient detection of important signals
in a wideband spectrum. In the work presented in this paper, it is assumed that
the modulation technique used is available as a priori knowledge of the
targeted important signal. The proposed spectral attention-driven intelligent
method is consists of two main components, a spectral correlation function
(SCF) based spectral visualization scheme and a spectral attention-driven
reinforcement learning mechanism that adaptively selects the spectrum range and
implements the intelligent signal detection. Simulations illustrate that the
proposed method can achieve high accuracy of signal detection while observation
of spectrum is limited to few ranges via effectively selecting the spectrum
ranges to be observed. Furthermore, the proposed spectral attention-driven
machine learning method can lead to an efficient adaptive intelligent spectrum
sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure
Machine learning in spectral domain
Deep neural networks are usually trained in the space of the nodes, by
adjusting the weights of existing links via suitable optimization protocols. We
here propose a radically new approach which anchors the learning process to
reciprocal space. Specifically, the training acts on the spectral domain and
seeks to modify the eigenvectors and eigenvalues of transfer operators in
direct space. The proposed method is ductile and can be tailored to return
either linear or non linear classifiers. The performance are competitive with
standard schemes, while allowing for a significant reduction of the learning
parameter space. Spectral learning restricted to eigenvalues could be also
employed for pre-training of the deep neural network, in conjunction with
conventional machine-learning schemes. Further, it is surmised that the nested
indentation of eigenvectors that defines the core idea of spectral learning
could help understanding why deep networks work as well as they do
On the Sample Complexity of Subspace Learning
A large number of algorithms in machine learning, from principal component
analysis (PCA), and its non-linear (kernel) extensions, to more recent spectral
embedding and support estimation methods, rely on estimating a linear subspace
from samples. In this paper we introduce a general formulation of this problem
and derive novel learning error estimates. Our results rely on natural
assumptions on the spectral properties of the covariance operator associated to
the data distribu- tion, and hold for a wide class of metrics between
subspaces. As special cases, we discuss sharp error estimates for the
reconstruction properties of PCA and spectral support estimation. Key to our
analysis is an operator theoretic approach that has broad applicability to
spectral learning methods.Comment: Extendend Version of conference pape
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
The rise of graph-structured data such as social networks, regulatory
networks, citation graphs, and functional brain networks, in combination with
resounding success of deep learning in various applications, has brought the
interest in generalizing deep learning models to non-Euclidean domains. In this
paper, we introduce a new spectral domain convolutional architecture for deep
learning on graphs. The core ingredient of our model is a new class of
parametric rational complex functions (Cayley polynomials) allowing to
efficiently compute spectral filters on graphs that specialize on frequency
bands of interest. Our model generates rich spectral filters that are localized
in space, scales linearly with the size of the input data for
sparsely-connected graphs, and can handle different constructions of Laplacian
operators. Extensive experimental results show the superior performance of our
approach, in comparison to other spectral domain convolutional architectures,
on spectral image classification, community detection, vertex classification
and matrix completion tasks
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