2,844 research outputs found
Interpolation of Sparse Graph Signals by Sequential Adaptive Thresholds
This paper considers the problem of interpolating signals defined on graphs.
A major presumption considered by many previous approaches to this problem has
been lowpass/ band-limitedness of the underlying graph signal. However,
inspired by the findings on sparse signal reconstruction, we consider the graph
signal to be rather sparse/compressible in the Graph Fourier Transform (GFT)
domain and propose the Iterative Method with Adaptive Thresholding for Graph
Interpolation (IMATGI) algorithm for sparsity promoting interpolation of the
underlying graph signal.We analytically prove convergence of the proposed
algorithm. We also demonstrate efficient performance of the proposed IMATGI
algorithm in reconstructing randomly generated sparse graph signals. Finally,
we consider the widely desirable application of recommendation systems and show
by simulations that IMATGI outperforms state-of-the-art algorithms on the
benchmark datasets in this application.Comment: 12th International Conference on Sampling Theory and Applications
(SAMPTA 2017
Image classification by visual bag-of-words refinement and reduction
This paper presents a new framework for visual bag-of-words (BOW) refinement
and reduction to overcome the drawbacks associated with the visual BOW model
which has been widely used for image classification. Although very influential
in the literature, the traditional visual BOW model has two distinct drawbacks.
Firstly, for efficiency purposes, the visual vocabulary is commonly constructed
by directly clustering the low-level visual feature vectors extracted from
local keypoints, without considering the high-level semantics of images. That
is, the visual BOW model still suffers from the semantic gap, and thus may lead
to significant performance degradation in more challenging tasks (e.g. social
image classification). Secondly, typically thousands of visual words are
generated to obtain better performance on a relatively large image dataset. Due
to such large vocabulary size, the subsequent image classification may take
sheer amount of time. To overcome the first drawback, we develop a graph-based
method for visual BOW refinement by exploiting the tags (easy to access
although noisy) of social images. More notably, for efficient image
classification, we further reduce the refined visual BOW model to a much
smaller size through semantic spectral clustering. Extensive experimental
results show the promising performance of the proposed framework for visual BOW
refinement and reduction
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical
Sciences
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