A review of feature selection and its methods
B Venkatesh, J Anuradha - Cybernetics and information technologies, 2019 - sciendo.com
Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
In applications such as social, energy, transportation, sensor, and neuronal networks, high-
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …
Point convolutional neural networks by extension operators
This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for
applying convolutional neural networks to point clouds. The framework consists of two …
applying convolutional neural networks to point clouds. The framework consists of two …
Convolutional neural networks on graphs with fast localized spectral filtering
M Defferrard, X Bresson… - Advances in neural …, 2016 - proceedings.neurips.cc
In this work, we are interested in generalizing convolutional neural networks (CNNs) from
low-dimensional regular grids, where image, video and speech are represented, to high …
low-dimensional regular grids, where image, video and speech are represented, to high …
Learning Laplacian matrix in smooth graph signal representations
The construction of a meaningful graph plays a crucial role in the success of many graph-
based representations and algorithms for handling structured data, especially in the …
based representations and algorithms for handling structured data, especially in the …
Human brain networks function in connectome-specific harmonic waves
S Atasoy, I Donnelly, J Pearson - Nature communications, 2016 - nature.com
A key characteristic of human brain activity is coherent, spatially distributed oscillations
forming behaviour-dependent brain networks. However, a fundamental principle underlying …
forming behaviour-dependent brain networks. However, a fundamental principle underlying …
Graph spectral image processing
Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …
that live naturally on irregular data kernels described by graphs (eg, social networks …
Wavelets on graphs via spectral graph theory
DK Hammond, P Vandergheynst… - Applied and Computational …, 2011 - Elsevier
We propose a novel method for constructing wavelet transforms of functions defined on the
vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling …
vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling …
Spectral clustering and the high-dimensional stochastic blockmodel
Networks or graphs can easily represent a diverse set of data sources that are characterized
by interacting units or actors. Social networks, representing people who communicate with …
by interacting units or actors. Social networks, representing people who communicate with …
A tutorial on spectral clustering
U Von Luxburg - Statistics and computing, 2007 - Springer
In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra …
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra …