@inProceedings{
 title = {Semi-supervised visualization of high-dimensional data.},
 type = {inProceedings},
 year = {2004},
 id = {8e93b794-86d2-3206-90d8-38355df80d85},
 created = {2019-11-19T16:29:09.034Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:32:30.032Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:544},
 source_type = {inproceedings},
 notes = {Proc. of the 7th International Conference on Pattern Recognition and Image Analysis:<br/>New Information Technologies (PRIA-7-2004), St. Petersburg, Russia, 748-751.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 abstract = {High-dimensional data visualization is a more complex process than an ordinary dimensionality reduction to two or three dimensions. Therefore we propose and evaluate a novel four-step visualization approach that is built upon the combination of three components, namely, metric learning, intrinsic dimensionality estimation and feature extraction. Though many successful applications of dimensionality reduction techniques for visualization are known, we believe that the sophisticated nature of high-dimensional data often needs a combination of several machine learning methods to solve the task, which is provided by the framework in experiments with real-world data.},
 bibtype = {inProceedings},
 author = {Kouropteva, O and Okun, O and Pietikäinen, M}
}

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