no code implementations • 27 Sep 2022 • Kelum Gajamannage, Yonggi Park, Mallikarjunaiah Muddamallappa, Sunil Mathur
The applicability of GDD is limited as it encounters $\mathcal{O}(n^6)$ when denoising a given image of size $n\times n$ since GGD computes the prominent singular vectors of a $n^2 \times n^2$ data matrix that is implemented by singular value decomposition (SVD).
no code implementations • 4 Sep 2022 • Mary Isangediok, Kelum Gajamannage
For both phishing website URLs and credit card fraud transaction datasets, the results indicate that extreme gradient boost trained on the original data shows trustworthy performance in the imbalanced dataset and manages to outperform the other three methods in terms of both AUC ROC and AUC PR.
no code implementations • 10 May 2022 • Kelum Gajamannage, Yonggi Park
People have been using learning tools from diverse fields such as financial mathematics and machine learning in the attempt of making trustworthy predictions on such markets.
no code implementations • 4 Mar 2022 • Yonggi Park, Kelum Gajamannage, Alexey Sadovski
As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images.
no code implementations • 14 Feb 2022 • Yonggi Park, Kelum Gajamannage, Dilhani I. Jayathilake, Erik M. Bollt
Specifically, we analyze the performance of RNNs applied to three tasks: reconstruction of correct Lorenz solutions for a system with a formulation error, reconstruction of corrupted collective motion trajectories, and forecasting of streamflow time series possessing spikes, representing three fields, namely, ordinary differential equations, collective motion, and hydrological modeling, respectively.
no code implementations • 20 Oct 2021 • Kelum Gajamannage, Yonggi Park, Randy Paffenroth, Anura P. Jayasumana
Learning dynamics of collectively moving agents such as fish or humans is an active field in research.
no code implementations • 14 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 accurate images.
no code implementations • 19 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.
no code implementations • 21 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.
no code implementations • 23 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.
no code implementations • 13 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.
no code implementations • 12 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.