3 research outputs found
Learning Product Graphs from Spectral Templates
Graph Learning (GL) is at the core of inference and analysis of connections
in data mining and machine learning (ML). By observing a dataset of graph
signals, and considering specific assumptions, Graph Signal Processing (GSP)
tools can provide practical constraints in the GL approach. One applicable
constraint can infer a graph with desired frequency signatures, i.e., spectral
templates. However, a severe computational burden is a challenging barrier,
especially for inference from high-dimensional graph signals. To address this
issue and in the case of the underlying graph having graph product structure,
we propose learning product (high dimensional) graphs from product spectral
templates with significantly reduced complexity rather than learning them
directly from high-dimensional graph signals, which, to the best of our
knowledge, has not been addressed in the related areas. In contrast to the rare
current approaches, our approach can learn all types of product graphs (with
more than two graphs) without knowing the type of graph products and has fewer
parameters. Experimental results on both the synthetic and real-world data,
i.e., brain signal analysis and multi-view object images, illustrate
explainable and meaningful factor graphs supported by expert-related research,
as well as outperforming the rare current restricted approaches.Comment: 10 figures, 12 pages, Submitted to IEEE Transactions on Signal and
Information Processing over Networks on 10-Oct-202
Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity
Numerous approaches have been proposed to discover causal dependencies in
machine learning and data mining; among them, the state-of-the-art VAR-LiNGAM
(short for Vector Auto-Regressive Linear Non-Gaussian Acyclic Model) is a
desirable approach to reveal both the instantaneous and time-lagged
relationships. However, all the obtained VAR matrices need to be analyzed to
infer the final causal graph, leading to a rise in the number of parameters. To
address this issue, we propose the CGP-LiNGAM (short for Causal Graph
Process-LiNGAM), which has significantly fewer model parameters and deals with
only one causal graph for interpreting the causal relations by exploiting Graph
Signal Processing (GSP)
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation
The classification of sleep stages plays a crucial role in understanding and
diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual
inspection by an expert that is time consuming and subjective procedure.
Recently, deep learning neural network approaches have been leveraged to
develop a generalized automated sleep staging and account for shifts in
distributions that may be caused by inherent inter/intra-subject variability,
heterogeneity across datasets, and different recording environments. However,
these networks ignore the connections among brain regions, and disregard the
sequential connections between temporally adjacent sleep epochs. To address
these issues, this work proposes an adaptive product graph learning-based graph
convolutional network, named ProductGraphSleepNet, for learning joint
spatio-temporal graphs along with a bidirectional gated recurrent unit and a
modified graph attention network to capture the attentive dynamics of sleep
stage transitions. Evaluation on two public databases: the Montreal Archive of
Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night
polysomnography recordings of 62 and 20 healthy subjects, respectively,
demonstrates performance comparable to the state-of-the-art (Accuracy:
0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database
respectively). More importantly, the proposed network makes it possible for
clinicians to comprehend and interpret the learned connectivity graphs for
sleep stages.Comment: 9 pages, 6 figure