22 accepted papers
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map simply to ones in a simi…
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems…
In this paper, we propose a method, based on graph signal processing, to optimize the choice of k in k-nearest neighbor graphs (kNNGs). kNN is one of the most popular approaches and is widely used in machine learning and signal processing. The parameter k represents the number of neighbors that are…
In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning technique…
Finding important edges in a graph is a crucial problem for various research fields such as network epidemics, signal processing, machine learning, and sensor networks. In this paper, we tackle the problem based on sampling theory on graphs. We convert the original graph to a line graph where its no…
In this paper, we propose a parameter selection method for graph learning. Graph learning, a technique of learning graphs from observations, is required in many applications, e.g., classification, prediction, and clustering. However, there is no established method to determine hyperparameters that c…
We propose a denoising method of multimodal graph signals with twofold smoothness regularization. Graph signal processing assumes that a signal has an underlying structure that is represented by a graph. In each node of the graph, we often have multimodal data or features that are correlated across…
We propose a sampling set selection method for graph signals under arbitrary signal priors. Most approaches of graph signal sampling assume that signals are bandlimited. However, in practical situations, there exist many full-band graph signals like piecewise smooth/constant signals. Our sampling se…
We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph s…
In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM (PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimiz…
We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and assuming an appropriate prior. In this paper, w…
We propose ScalpNet: A deep neural network to detect spatiotemporal abnormal intervals from EEGs of epilepsy patients. Since the number of trained clinicians is very limited, it is very crucial to establish automatic detection of abnormal signals caused by epilepsy from EEGs. We build a convolutiona…
Signals defined on a network or a graph are often prone to errors due to missing data and noise. In order to restore the graph signal, interpolation and denoising are two necessary steps along with other graph signal processing procedures. However, existing graph signal interpolation and denoising m…
We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The problem is formulated as a convex optimization problem to impose a constraint on the temporal relat…
This paper presents a framework for perfect reconstruction two-channel critically-sampled graph filter banks with spectral domain sampling. Graph signals have a unique characteristic: sampling in the vertex and graph spectral domains are generally different, in contrast to classical signal processin…
This paper addresses a polyphase <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> structure of spectral graph wavelets and filter banks. We consider two-channel critically sampled graph filter banks. In classical signal processing, polyphase stru…
This paper addresses the problem of finding optimal sensor placement, i.e., determining F sensor positions from N possible locations. We propose a sensor selection method based on the localization operator of graph signal processing. This method can select sensors while considering the localizations…
Directional block transforms (DBTs), such as discrete Fourier transforms, are basically less efficient for sparse image representation than directional overlapped transforms, such as curvelet and contourlet, but have advantages in practical computation, such as less computational cost, less amount o…
We propose an eigenvalue shrinkage method with a modified Chebyshev polynomial approximation (CPA). The eigenvalue shrinkage has been used in many fields of signal and image processing. However, the shrinkage takes enormous computation time especially in the case that a matrix constructed from a sig…
We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the exist…
We propose an image colorization method using fast soft-thresholding of singular values (singular value thresholding). An image colorization method with nuclear norm minimization (NNM) has been proposed and brings good results. NNM usually requires iterative application of singular value decompositi…
This paper presents a design method of critically sampled graph wavelet transforms (CSGWTs) utilizing real-valued biorthogonal linear-phase wavelets for regular signals. Their filter characteristics are equivalent to those of biorthogonal linear-phase wavelets and can be expressed by real-valued clo…