Elif Vural

E-mail
velif@metu.edu.tr
Department
Department of Electrical and Electronics Engineering
Scopus Author ID
Web of Science Researcher ID
Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme
Acar, Abdullah Burak; Vural, Elif (2022-01-01)
© 2022 IEEE.We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral ...
Estimation of Locally Stationary Graph Processes from Incomplete Realizations
Canbolat, Abdullah; Vural, Elif (2022-01-01)
© 2022 IEEE.Stationarity is a well-studied concept in signal processing and the concept of stationary random processes has been extended to graph domains in several recent works. Meanwhile, in many scenarios a globally sta...
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi
Furkan Kar, Osman; Turhan, Gülce; Vural, Elif (2022-01-01)
© 2022 IEEE.In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband gr...
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals
Acar, Abdullah Burak; Vural, Elif (2022-01-01)
© 2022 IEEE.In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying ...
Learning Graph Signal Representations with Narrowband Spectral Kernels
Kar, Osman Furkan; Turhan, Gülce; Vural, Elif (2022-01-01)
© 2022 IEEE.In this work, we study the problem of learning graph dictionary models from partially observed graph signals. We represent graph signals in terms of atoms generated by narrowband graph kernels. We formulate an ...
A Theoretical Analysis of Multi-Modal Representation Learning with Regular Functions
Vural, Elif (2021-01-07)
Multi-modal data analysis methods often learn representations that align different modalities in a new common domain, while preserving the within-class compactness and within-modality geometry and enhancing the between-cla...
Investigation of Stationarity for Graph Time Series Data Sets
Güneyi, Eylem Tuğçe; Vural, Elif (2021-01-07)
Graphs permit the analysis of the relationships in complex data sets effectively. Stationarity is a feature that facilitates the analysis and processing of random time signals. Since graphs have an irregular structure, the...
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm
Kaya, Semih; Vural, Elif (2021-01-01)
While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear embeddings to previously unseen data is a ra...
Estimating Partially Observed Graph Signals by Learning Spectrally Concentrated Graph Kernels
Turhan, Gulce; Vural, Elif (2021-01-01)
© 2021 IEEE.Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not...
Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations
Guneyi, Eylem Tugce; Canbolat, Abdullah; Vural, Elif (2021-01-01)
© 2021 IEEE.We consider the problem of estimating time-varying graph signals with missing observations, which is of interest in many applications involving data acquisition on irregular topologies. We model time-varying gr...
Citation Formats