Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Kopriva, Ivica"'
Autor:
Kopriva, Ivica, Sitnik, Dario, Dion-Bertrand, Laura-Isabelle, Periša, Marija Milković, Hadžija, Mirko, Hadžija, Marijana Popović
Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the
Externí odkaz:
http://arxiv.org/abs/2411.06969
Autor:
Kopriva, Ivica, Sersic, Damir
Subspace clustering (SC) algorithms utilize the union of subspaces model to cluster data points according to the subspaces from which they are drawn. To better address separability of subspaces and robustness to noise we propose a wavelet packet (WP)
Externí odkaz:
http://arxiv.org/abs/2406.03819
Autor:
Kopriva, Ivica
Kernel methods are applied to many problems in pattern recognition, including subspace clustering (SC). That way, nonlinear problems in the input data space become linear in mapped high-dimensional feature space. Thereby, computationally tractable no
Externí odkaz:
http://arxiv.org/abs/2401.17035
Autor:
Sindičić, Lovro, Kopriva, Ivica
Deep subspace clustering (DSC) networks based on self-expressive model learn representation matrix, often implemented in terms of fully connected network, in the embedded space. After the learning is finished, representation matrix is used by spectra
Externí odkaz:
http://arxiv.org/abs/2401.17033
Autor:
Sitnik, Dario, Kopriva, Ivica
Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs pos
Externí odkaz:
http://arxiv.org/abs/2204.06955
Autor:
Sitnik, Dario, Kopriva, Ivica
Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which is hard to interpret. Their generalization ability can be reduced because of the lack of pixel wise annotated
Externí odkaz:
http://arxiv.org/abs/2203.03636
This paper proposes low tensor-train (TT) rank and low multilinear (ML) rank approximations for de-speckling and compression of 3D optical coherence tomography (OCT) images for a given compression ratio (CR). To this end, we derive the alternating di
Externí odkaz:
http://arxiv.org/abs/2008.11414
Autor:
Sitnik, Dario, Kopriva, Ivica
Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and classification problems. This pa
Externí odkaz:
http://arxiv.org/abs/2004.03375
Autor:
Brbić, Maria, Kopriva, Ivica
In many applications, high-dimensional data points can be well represented by low-dimensional subspaces. To identify the subspaces, it is important to capture a global and local structure of the data which is achieved by imposing low-rank and sparsen
Externí odkaz:
http://arxiv.org/abs/1812.06580
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF wi
Externí odkaz:
http://arxiv.org/abs/1709.10323