Search Results for author: Kelum Gajamannage

Found 6 papers, 0 papers with code

A Patch-based Image Denoising Method Using Eigenvectors of the Geodesics' Gramian Matrix

no code implementations14 Oct 2020 Kelum Gajamannage, Randy Paffenroth, Anura P. Jayasumana

Thus, here we propose a novel and computationally efficient image denoising method that is capable of producing an accurate output.

Image Denoising

Bounded Manifold Completion

no code implementations19 Dec 2019 Kelum Gajamannage, Randy Paffenroth

Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research.

Dimensionality Reduction Image Inpainting +2

A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics

no code implementations21 Jul 2017 Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt

Herein, we propose a framework for nonlinear dimensionality reduction that generates a manifold in terms of smooth geodesics that is designed to treat problems in which manifold measurements are either sparse or corrupted by noise.

Dimensionality Reduction

Detecting phase transitions in collective behavior using manifold's curvature

no code implementations23 Sep 2015 Kelum Gajamannage, Erik M. Bollt

If a given behavior of a multi-agent system restricts the phase variable to a invariant manifold, then we define a phase transition as change of physical characteristics such as speed, coordination, and structure.

Dimensionality Reduction of Collective Motion by Principal Manifolds

no code implementations13 Aug 2015 Kelum Gajamannage, Sachit Butail, Maurizio Porfiri, Erik M. Bollt

Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates.

Dimensionality Reduction

Identifying manifolds underlying group motion in Vicsek agents

no code implementations12 Aug 2015 Kelum Gajamannage, Sachit Butail, Maurizio Porfiri, Erik M. Bollt

In a topological sense, we describe these changes as switching between low-dimensional embedding manifolds underlying a group of evolving agents.

Dimensionality Reduction

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