@inproceedings{
 title = {Modelling data segmentation for image retrieval systems},
 type = {inproceedings},
 year = {2009},
 pages = {201-208},
 volume = {533},
 id = {98d364d4-7824-32bb-8f8f-3124ad4c0b98},
 created = {2022-08-29T17:43:21.929Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:21.929Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The analysis of a large amount of data with complex structures is a challenging task in engineering and scientific applications. The data segmentation task usually involves either probabilistic or statistical approaches, however, global minima disadvantage is still present in each mentioned approaches. A combination of these two approaches is based on subspace arrangements avoiding global minima in classification methods. In this paper we propose a new approach for a generalized principal component analysis algorithm (GPCA) improving knowledge representation in images. The linear algebra concepts achieve an abstraction of data sets whose items as images, documents or stellar spectra could be handled providing a knowledge description for image classification process of involved data. We describe a solution to optimization GPCA function using Gutmann Algorithm for segmentation data sets.},
 bibtype = {inproceedings},
 author = {Flores-Pulido, Leticia and Starostenko, Oleg and Rodrguez-Gomez, Gustavo and Alarcon-Aquino, Vicente},
 booktitle = {CEUR Workshop Proceedings}
}

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