8 research outputs found
<Bioinformatics Center>Bio-knowledge Engineering
This Annual Report covers from 1 January to 31 December 202
<Bioinformatics Center>Bio-knowledge Engineering
This Annual Report covers from 1 January to 31 December 202
<Bioinformatics Center>Bio-knowledge Engineering
This Annual Report covers from 1 January to 31 December 202
Hypergraph Structure Inference From Data Under Smoothness Prior
Hypergraphs are important for processing data with higher-order relationships
involving more than two entities. In scenarios where explicit hypergraphs are
not readily available, it is desirable to infer a meaningful hypergraph
structure from the node features to capture the intrinsic relations within the
data. However, existing methods either adopt simple pre-defined rules that fail
to precisely capture the distribution of the potential hypergraph structure, or
learn a mapping between hypergraph structures and node features but require a
large amount of labelled data, i.e., pre-existing hypergraph structures, for
training. Both restrict their applications in practical scenarios. To fill this
gap, we propose a novel smoothness prior that enables us to design a method to
infer the probability for each potential hyperedge without labelled data as
supervision. The proposed prior indicates features of nodes in a hyperedge are
highly correlated by the features of the hyperedge containing them. We use this
prior to derive the relation between the hypergraph structure and the node
features via probabilistic modelling. This allows us to develop an unsupervised
inference method to estimate the probability for each potential hyperedge via
solving an optimisation problem that has an analytical solution. Experiments on
both synthetic and real-world data demonstrate that our method can learn
meaningful hypergraph structures from data more efficiently than existing
hypergraph structure inference methods
ICR ANNUAL REPORT 2020 (Volume 27)[All Pages]
This Annual Report covers from 1 January to 31 December 202
ICR ANNUAL REPORT 2022 (Volume 29)[All Pages]
This Annual Report covers from 1 January to 31 December 202
Learning on Hypergraphs With Sparsity
Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed. On a hypergraph, as a generalization of graph, one wishes to learn a smooth function with respect to its topology. A fundamental issue is to find suitable smoothness measures of functions on the nodes of a graph/hypergraph. We show a general framework that generalizes previously proposed smoothness measures and also generates new ones. To address the problem of irrelevant or noisy data, we wish to incorporate sparse learning framework into learning on hypergraphs. We propose sparsely smooth formulations that learn smooth functions and induce sparsity on hypergraphs at both hyperedge and node levels. We show their properties and sparse support recovery results. We conduct experiments to show that our sparsely smooth models are beneficial to learning irrelevant and noisy data, and usually give similar or improved performances compared to dense models.Peer reviewe