4 papers
Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks
Süha Tuna, Ülker Başar
Feature extraction for hyperspectral image classification is conventionally addressed using rigid tensor decompositions that fail to capture complex spatio-spectral interdependenci…
Efficient Spatial-Spectral Feature Extraction in Hyperspectral Images via Holistic Multivariance Decomposition
Süha Tuna
Tensor decomposition serves as a foundational tool for feature extraction in hyperspectral image classification, a domain classically dominated by the Tucker and Canonical Polyadic…
Holistic Multivariance Decomposition: Adapting Mode Interrelations in Low-Rank Tensor Approximations
Süha Tuna
Low-rank tensor approximation is a foundational tool for multidimensional data analysis in scientific computing, classically dominated by Tucker and Canonical Polyadic (CP) decompo…
Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling
Suha Tuna, Cagri Gulec, Emrah Yucesan +2
In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specif…