Yigit Oktar

Izmir University of Economics

In case of high dimensionality, a class of data clustering methods has been proposed as a solution that includes suitable subspace search to find inherent clusters. Sparsity-based clustering approaches include a twist in subspace approach as they incorporate a dimensionality expansion through the usage of an overcomplete dictionary representation....

It has already been proven that under certain circumstances dictionary learning for sparse representations is equivalent to conventional k-means clustering. Through additional modifications on sparse representations, it is possible to generalize the notion of centroids to higher orders. In a related algorithm which is called k-flats, q-dimensional...

Dictionary learning for sparse representations has been successful in many reconstruction tasks. Simplicial learning is an adaptation of dictionary learning, where subspaces become clipped and acquire arbitrary offsets, taking the form of simplices. Such adaptation is achieved through additional constraints on sparse codes. Furthermore, an evolutio...

Graph isomorphism is an important problem as its worst-case time complexity is not yet fully understood. In this study, we try to draw parallels between a related optimization problem called point set registration. A graph can be represented as a point set in enough dimensions using a simplex embedding and sampling. Given two graphs, the isomorphis...

Her hücrenin bir tamsayıya, dolayısıyla olası bir toplama karşılık geldiği ayrık hücreli bir bant dikkate alınarak, bir NP-tam problemi olan ve bu çalışma için temel taşı uygulaması olan alt küme toplamı problemine, kaydırmalar ve eleman bazında toplamlar kullanılarak sözde polinom bir çözüm verilebilir. Bu mekanizma, her olası toplamın frekans ban...

In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is orthogonal to each other and thus there is no distinction of in-between relations of dimensions. However, this is certainly not the case in real world signals which naturally originate from a spatio-temporal c...

Dictionary learning for sparse representations is generative in nature, hence discriminative modifications are commonly observed for classification problems. Classical dictionary learning bears a fundamental problem of not being capable of distinguishing two different classes lying on the same subspace, that cannot be resolved by any discriminative...

It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investi...

Dictionary learning is conventionally utilized as a feature learning method. Such framework is commonly used in reconstructive signal processing tasks. Learnt features can also be used as inputs to further classification and clustering schemes. Using block-sparsity, sparse framework can be cast as a clustering problem directly. In its conventional...

In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is orthogonal to each other and thus there is no distinction of in-between relations of dimensions. However, this is certainly not the case in real world signals which naturally originate from a spatio-temporal c...

Dictionary learning for sparse representations has been successful in many reconstruction tasks. Simplicial learning is an adaptation of dictionary learning, where subspaces become clipped and acquire arbitrary offsets, taking the form of simplices. Such adaptation is achieved through additional constraints on sparse codes. Furthermore, an evolutio...

In standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. However, many objections are raised against the validity of this argument. Such objections claim that Turing test is not a tool to demonstrate existence of general...

In standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. However, many objections are raised against the validity of this argument. Such objections claim that Turing test is not a tool to demonstrate existence of general...

It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investi...

It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investi...

A concise presentation for the concept of k-polytopes

Integration of intelligent systems in fashion and design related field is a relatively new concept. The aim of this research was to evaluate the performance of an intelligent fashion styling recommendation system for non-standard female body shapes. As a recommender strategy, the intelligent fashion styling system employs a two-stage genetic search...

PointCrust is a WebGL tool, based on three.js library, that converts a point cloud dataset to a triangle mesh structure. Berger et al. present a nice review on state of the art methods for surface reconstruction from point clouds. There are web tools for point cloud to mesh approximation such as MeshLab, however there is no dedicated tool for this...

Machine learning usage in the neurosciences has been explosively increased for the past three years. Besides strengths and also the inherent misusage of machine learning in neurosciences, the results obtained from these studies suggest that machine learning methods may extensively be used for the clinical diagnosis and for the investigation of diff...