5 citations · 7 across the 10 of their papers we have counts for
8 papers · 1 filter
Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection
Shervin Rahimzadeh Arashloo
One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a pointwise fashion and ignore the geom…
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection
Sepehr Nourmohammadi, Arda Sarp Yenicesu, Shervin Rahimzadeh Arashloo +1
This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic p-norm constraints. We introduce a framework that dynamical…
Lp-Norm Constrained One-Class Classifier Combination
Sepehr Nourmohammadi, Shervin Rahimzadeh Arashloo
Classifier fusion is established as an effective methodology for boosting performance in different settings and one-class classification is no exception. In this study, we consider…
Slack Norm Support Vector Data Description
Shervin R. Arashloo
The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere t…
-Norm Multiple Kernel One-Class Fisher Null-Space
Shervin Rahimzadeh Arashloo
The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space one-class classification princip…
Multi-Task Kernel Null-Space for One-Class Classification
Shervin Rahimzadeh Arashloo, Josef Kittler
The one-class kernel spectral regression (OC-KSR), the regression-based formulation of the kernel null-space approach has been found to be an effective Fisher criterion-based metho…
Robust One-Class Kernel Spectral Regression
Shervin Rahimzadeh Arashloo, Josef Kittler
The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally at…
One-Class Kernel Spectral Regression
Shervin Rahimzadeh Arashloo, Josef Kittler
The paper introduces a new efficient nonlinear one-class classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilber…