30,914 research outputs found
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
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Information filtering via preferential diffusion
Recommender systems have shown great potential to address information
overload problem, namely to help users in finding interesting and relevant
objects within a huge information space. Some physical dynamics, including heat
conduction process and mass or energy diffusion on networks, have recently
found applications in personalized recommendation. Most of the previous studies
focus overwhelmingly on recommendation accuracy as the only important factor,
while overlook the significance of diversity and novelty which indeed provide
the vitality of the system. In this paper, we propose a recommendation
algorithm based on the preferential diffusion process on user-object bipartite
network. Numerical analyses on two benchmark datasets, MovieLens and Netflix,
indicate that our method outperforms the state-of-the-art methods.
Specifically, it can not only provide more accurate recommendations, but also
generate more diverse and novel recommendations by accurately recommending
unpopular objects.Comment: 12 pages, 10 figures, 2 table
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