6 accepted papers
In our recent work we have proposed a new class of graph signal expansions termed local graph Fourier frames (LGFFs). LGFFs have finite support in the vertex domain and hence entail computationally highly efficient signal analysis and synthesis algorithms. Furthermore, they are extremely flexible an…
Graph signal processing (GSP) has provided a wide range of powerful methodologies for diverse learning tasks. While the data domain in GSP is fundamentally non-Euclidean, the relation between graph signal samples has mostly been studied using Euclidean similarity metrics. In our recent work on signa…
The novel concept of signature graphs extends signed graphs by admitting multiple types of partial similarity/agreement or dissimilarity/disagreement. Extending the concept of balancedness to signature graphs yields an explicit and efficient basis for multi-class clustering and classification. Contr…
Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via…
We consider the problem of semi-supervised clustering for multiple (more than two) classes. The proposed clustering algorithm uses the (dis)similarity of given data to learn the unknown cluster labels. We quantify label (dis)similarity in terms of the new concept of signed total variation (TV). The…
We consider the problem of learning the topology of a graph from a given set of smooth graph signals. We construct a weighted adjacency matrix that best explains the data in the sense of achieving the smallest graph total variation. For the case of noisy measurements of the graph signals we propose…