8 accepted papers
Matrix graph signals, which are associated with two factor graphs, are ubiquitous in daily life, such as time-varying physical signals in sensor networks and rating matrices in recommendation systems. In practice, due to the row-wise and column-wise smoothness, they are modeled as bandlimited (BL) g…
Graph-based multi-armed bandit algorithms utilize the relationship between users to select the best item to recommend for maximal reward, which is decided by items’ features and un-known users’ preferences. Therefore, the precise estimation of users’ preferences is fairly important and indispensable…
Sampling of bandlimited (BL) graph signals is one fundamental problem in graph signal processing (GSP), whose underlying kernel is an irregular graph rather than regular 1-D time-series kernel in classical discrete signal processing (DSP). Though there were amounts of sampling objectives and algorit…
This paper proposes a new model, called hierarchical and multi-view dependency modelling network (HMVDM), for the task of emotion recognition in conversations (ERC). The modelling of conversational context plays an important role in ERC, especially for the multi-turn and multi-speaker conversations…
Manifold landmarking is the problem of selecting a subset of discrete locations on a continuous manifold for label assignment, in order to reduce interpolation error of subsequent semi-supervised learning. In this paper, we select landmarks to minimize the condition number (λ <inf xmlns:mml="http://…
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph signal compression and denoising. Previous graph learning algorithms either i) make assumptions on graph connectivity (e.g., graph sparsity), or ii) make edge weight assumptions such as…
Graph sampling with independent noise towards minimum mean square error (MMSE) leads to the known A-optimality criterion, which is computation-intensive to evaluate and NP-hard to optimize. In this paper, we propose a new low-complexity sampling strategy based on Neumann series that circumvents larg…
Graph sampling with noise is a fundamental problem in graph signal processing (GSP). Previous works assume an unbiased least square (LS) signal reconstruction scheme and select samples greedily via expensive extreme eigenvector computation. A popular biased scheme using graph Laplacian regularizatio…