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Found 6 papers, 1 papers with code
Joint Graph and Vertex Importance Learning
In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data.
Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite Variation Operators
Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator.
Spectral folding and two-channel filter-banks on arbitrary graphs
A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs.
TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
Region adaptive graph fourier transform for 3d point clouds
Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient.
Generating Labels for Regression of Subjective Constructs using Triplet Embeddings
In this work, we propose a novel annotation approach using triplet embeddings.
