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- Nikolaos Tsapanos, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
- Pattern Recognition
- 2015

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. Kernel k-Means is a state of the art clustering algorithm. However, in contrast to clustering algorithms that can work using only a limited… (More)

- Nikolaos Tsapanos, Anastasios Tefas, Ioannis Pitas
- Image Vision Comput.
- 2010

- Nikolaos Tsapanos, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
- Pattern Recognition
- 2012

In this paper, a novel algorithm for shape matching based on the Hausdorff distance and a binary search tree data structure is proposed. The shapes are stored in a binary search tree that can be traversed according to a Hausdorfflike similarity measure that allows us to make routing decisions at any given internal node. Each node functions as a classifier… (More)

- Nikolaos Tsapanos, Anastasios Tefas, Ioannis Pitas
- VISAPP
- 2008

- Nikolaos Tsapanos, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
- 2015 IEEE International Conference on Image…
- 2015

The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus overcoming the classic algorithm's inability to handle data that are not linearly separable. Given a set of n elements to cluster, the n × n kernel matrix is… (More)

- Nikolaos Tsapanos, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
- 2015 IEEE Symposium Series on Computational…
- 2015

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are… (More)

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are… (More)

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are… (More)

Kernel k-Means is a basis for many state of the art global clustering approaches. When the number of samples grows too big, however, it is extremely time-consuming to compute the entire kernel matrix and it is impossible to store it in the memory of a single computer. The algorithm of Approximate Kernel k-Means has been proposed, which works using only a… (More)

- Nikolaos Tsapanos, Anastasios Tefas, Ioannis Pitas
- ICANN
- 2010

In this paper, we present a system capable of dynamically learning shapes in a way that also allows for the dynamic deletion of shapes already learned. It uses a self-balancing Binary Search Tree (BST) data structure in which we can insert shapes that we can later retrieve and also delete inserted shapes. The information concerning the inserted shapes is… (More)