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Ahmad Pourdarvish

Ahmad Pourdarvish

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
HIndex:
Faculty: Faculty of Mathematical Sciences
Address:
Phone: 01135302470

Research

Title
A Novel Angle-Based Learning Framework on Semi-supervised Dimensionality Reduction in High-Dimensional Data with Application to Action Recognition
Type
JournalPaper
Keywords
High-dimensional data · Dimensionality reduction · Human factor · Angle-based discriminant · Scatter balance
Year
2020
Journal the arabian journal for science and engineering
DOI
Researchers Zahra Ramezani ، Ahmad Pourdarvish ، Kiumars Teymourian

Abstract

The existing outliers in high-dimensional data create various challenges to classify datasets such as the exact classification with imbalanced scatters. In this paper, we propose an angle-based framework as Angle Global and Local Discriminant Analysis (AGLDA) to consider imbalanced scatters. AGLDA chooses an optimal subspace by using angle cosine to achieve appropriate scatter balance in the dataset. The privilege of this method is to classify datasets with the effect of outliers by finding optimal subspace in high-dimensional data. Generally, this method is more effective and more reliable than other methods to classify data when there are outliers. Besides, human posture classification has been used as an application of the balanced semi-supervised dimensionality reduction to assist human factor experts and designers of industrial systems for diagnosing the type of maintenance crew postures. The experimental results show the efficiency of the proposed method via two real case studies, and the results have also been verified by comparing it with other approaches