no code implementations • 14 Feb 2023 • Eylem Tugce Guneyi, Berkay Yaldiz, Abdullah Canbolat, Elif Vural
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants.
no code implementations • 3 Jun 2020 • Semih Kaya, Elif Vural
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 rather overlooked subject.
no code implementations • 7 Nov 2019 • Yusuf Yigit Pilavci, Eylem Tugce Guneyi, Cemil Cengiz, Elif Vural
We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs.
1 code implementation • 22 Oct 2019 • Eda Bayram, Dorina Thanou, Elif Vural, Pascal Frossard
Structure inference is an important task for network data processing and analysis in data science.
no code implementations • 17 Dec 2018 • Elif Vural
We first propose a theoretical analysis of domain adaptation on graphs and present performance bounds that characterize the target classification error in terms of the properties of the graphs and the data manifolds.
no code implementations • 14 Mar 2018 • Mehmet Pilanci, Elif Vural
Although the semi-supervised estimation of class labels is an ill-posed problem in general, in several applications it is possible to find a source graph on which the label function has similar frequency content to that on the target graph where the actual classification problem is defined.
no code implementations • 28 Feb 2018 • Jeremy Aghaei Mazaheri, Elif Vural, Claude Labit, Christine Guillemot
A multilevel tree-structured discriminative dictionary is learnt for each class, with a learning objective concerning the reconstruction errors of the image patches around the pixels over each class-representative dictionary.
no code implementations • 19 Oct 2017 • Cem Ornek, Elif Vural
In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms.
no code implementations • 21 Jul 2015 • Elif Vural, Christine Guillemot
Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different classes.
no code implementations • 6 May 2015 • Julio Cesar Ferreira, Elif Vural, Christine Guillemot
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications.
no code implementations • 9 Feb 2015 • Elif Vural, Christine Guillemot
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding.
no code implementations • 11 Jan 2014 • Elif Vural, Pascal Frossard
As theoretical studies about the tangent distance algorithm have been largely overlooked, we present in this work a detailed performance analysis of this useful algorithm, which can eventually help its implementation.
no code implementations • 15 Feb 2013 • Elif Vural, Pascal Frossard
We show that the area of this neighborhood increases at least quadratically with the smoothing filter size, which justifies the use of a smoothing step in image registration with local optimizers such as gradient descent.