Wavelet-regularized graph semi-supervised learning

By Venkatesan N. Ekambaram, G. Fanti, Babak Ayazifar, Kannan Ramchandran. 2013

33 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Label Efficient Semi-Supervised Learning via Graph Filtering (2019, background): “It was shown in [21, 23] that they can be interpreted as low-pass graph filtering.”
Reliable Data Distillation on Graph Convolutional Network (2020, background): “Besides, as a type of low-pass graph filtering [18], the label propagation [62] and its variants [5, 61] are also very popular.”
Signal Recovery on Graphs: Variation Minimization (2014, background): “…involves graph-based ﬁltering [3], [7], [8], graph-based transforms [3], [6], [9], sampling and interpolation on graphs [10]–[12], uncertainty principle on graphs [13], semi-supervised classiﬁcation on graphs [14]–[16], graph dictionary learning [17], [18], and community detection on graphs [19].”
Discrete Signal Processing on Graphs: Sampling Theory (2015, background): “Recent work includes graph-based filtering [5]–[7], graph-based transforms [5], [8], [9], sampling and interpolation on graphs [10]–[12], uncertainty principle on graphs [13], semi-supervised classification on graphs [14]–[16], graph dictionary learning [17], [18], denoising [6], [19], community det”