$K$-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph Learning

Araghi, Hesam; Sabbaqi, Mohammad; Babaie‐Zadeh, Massoud

doi:10.1109/lsp.2019.2936665

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…In this paper, considering that when we learn a global graph for speech signals, it would be complex. Inspired by K-means and K-graphs learning [44], we partition the SGSs into clusters and use the K-graphs learning method to capture the potential properties of speech samples both the inter-frame and intra-frame. Noisy speech signals are mapped into the graph domain by using Eq.…”

Section: The K-graphs Learning Methods For Speech Graph Signalsmentioning

confidence: 99%

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An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

Wang

Guo

et al. 2023

J AUDIO SPEECH MUSIC PROC.

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The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit from the learned multiple graphs’ property and enhance interpretability, we study the spectral property of speech samples in the joint vertex-frequency domain by using the new graph weight matrix. Moreover, we propose the representation of minimum mean-square error (MMSE) graph spectral magnitude estimator for speech signals residing on undirected multiple graphs. We use the MMSE graph spectral magnitude estimator to improve speech enhancement performance. The numerical simulation results show that the proposed method outperforms the existing methods in graph signal processing (GSP) and the baseline methods for speech enhancement in discrete signal processing (DSP) in terms of PESQ, LLR, output SNR, and STOI results. These results also demonstrate the validity of the learned multiple graphs.

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Section: The K-graphs Learning Methods For Speech Graph Signalsmentioning

confidence: 99%

“…Let us now investigate the graph weighted matrix L k of G k , for the sake of revealing the intrinsic relationships among speech frames in real-time. ( 4) Following the K-graphs learning framework in [44], we formulate the multiple graphs learning problem of noisy speech graph signals as…”

Section: The K-graphs Learning Methods For Speech Graph Signalsmentioning

confidence: 99%

An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

Wang

Guo

et al. 2023

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…A more challenging problem is the inference of multiple networks where it is not known from which network observed data are generated. We note GSP methods [32], [43]- [45] and statistical methods [46]- [48] within this category. We refer the reader to the reviews in [10], [49], [50] for more examples of multiple network estimation.…”

Section: Related Workmentioning

confidence: 99%

Joint Network Topology Inference via a Shared Graphon Model

Madeline¹,

Segarra²

2022

Preprint

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We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is a nonparametric model from which graphs of potentially different sizes can be drawn. The versatility of graphons allows us to tackle the joint inference problem even for the cases where the graphs to be recovered contain different number of nodes and lack precise alignment across the graphs. Our solution is based on combining a maximum likelihood penalty with graphon estimation schemes and can be used to augment existing network inference methods. The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information. We validate our proposed approach by comparing its performance against competing methods in synthetic and realworld datasets.

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“…In the first setup, multiple datasets are given and each dataset is defined on a view [26], [35]. On the other hand, the second setup deals with the mixture of graph signals, where one is given a single dataset and the association of graph signals to the views is not known [36], [37], [38]. The focus of the present paper is the first category.…”

Section: Related Workmentioning

confidence: 99%

Multiview Graph Learning for single-cell RNA sequencing data

Karaaslanli

Saha

Aviyente

et al. 2021

Preprint

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Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

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$K$-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph Learning

Cited by 13 publications

References 13 publications

An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

Joint Network Topology Inference via a Shared Graphon Model

Multiview Graph Learning for single-cell RNA sequencing data

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