5 citations · 7 across the 10 of their papers we have counts for
11 papers
Deep Convolutional Large-Margin -SVDD for Visual Anomaly Detection
Alireza Dastmalchi Saei, Shervin Rahimzadeh Arashloo
Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are hig…
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…
Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression
Shervin Rahimzadeh Arashloo
The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that wer…
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…
Client-Specific Anomaly Detection for Face Presentation Attack Detection
Shervin Rahimzadeh Arashloo, Josef Kittler
The one-class anomaly detection approach has previously been found to be effective in face presentation attack detection, especially in an \textit{unseen} attack scenario, where th…
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…