Konstantinos Nikolaidis

Learn More
In instance-based machine learning, algorithms often suffer from high storage requirements because of the large number of training instances. This can result not only in large computer memory usage and long response time, but also very often in oversensitivity to noise. To tackle such problems, various instance reduction algorithms have been developed that(More)
This paper introduces a new collaborative feature extraction method based on projection pursuit with application to face recognition. We propose a new projection pursuit index based on the weighted sum of six state of the art indices. Using a genetic search, the hyperparameters of the proposed projection index as well as of the selected classifier were(More)
The operation of instance-based learning algorithms is based on storing a large set of prototypes in the system's database. However, such systems often experience issues with storage requirements, sensitivity to noise, and computational complexity, which result in high search and response times. In this brief, we introduce a novel framework that employs(More)
Principal components analysis has become a popular preprocessing method to avoid the small sample size problem for most of the supervised graph embedding methods. Nevertheless, there is potential loss of relevant information when projecting the data onto the space defined by the principal Eigenfaces when the number of individuals in the gallery is large.(More)
  • 1