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<span style="font-size: 14pt;"><b><i>"Fast Outlier Detection in Oblique Subspace"</i></b></span></div>
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<span style="font-size: 14pt;"><b>Bowen Li</b></span></div>
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<span style="font-size: 14pt;"></span><span style="font-size: 12pt;">Dept. of Computer Science,</span></div>
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Florida State University (FSU) </div>
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Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend. </div>
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NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants only. </div>
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<b>https://fsu.zoom.us/j/94273595552 </b></div>
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Meeting # <b>942 7359 5552 </b></div>
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🎦 Colloquium recordings will be made available here, <a class="OWAAutoLink" id="OWA1e1bdbc9-0dc4-4dd9-fe59-a0b78e8b7ac9" href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Ca893869c25ca4cb9c5b108de101db13c%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638965916859528297%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=XKbz5dgNMIr1927oXBjY0%2F%2FRLMRP6uZgJusAwFKniKM%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/colloquium">
https://www.sc.fsu.edu/colloquium</a> </div>
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<b>Wednesday, Oct 22, 2025, Schedule: </b></div>
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* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
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☕ Nespresso & Teatime - 417 DSL Commons </div>
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<b>* 3:30 to 4:30 PM Eastern Time (US and Canada) </b></div>
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<b>🕟 Colloquium - 499 DSL Seminar Room </b></div>
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<b>Abstract: </b></div>
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Subspace outlier detection is a fundamental data mining task for high-dimensional data. The existing hashing-based methods only effectively identify outliers in axis-parallel subspaces but fail on arbitrary-shaped, schema-less data such as time series and graphs.
We present OS-Hash (Oblique Subspace Hashing), a linear-time, constant-space solution that defines oblique subspaces based on pairwise object proximity, without requiring explicit multidimensional representations. By hashing data into arbitrarily oriented
subspaces, OS-Hash builds efficient histograms for outlier detection across diverse data types and extends naturally to the data stream setting.</div>
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Additional colloquium details can be found here,</div>
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<a class="OWAAutoLink" id="OWA61e31ad2-fb40-cf1e-df2d-b2940c3524bc" href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1886-colloquium-with-bowen-li-2025-10-22&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Ca893869c25ca4cb9c5b108de101db13c%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638965916859552366%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=VhXHGUy8QGhh%2FX5SPLzfMVBWksWwHdvdWKKSlofe1RY%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/news-and-events/colloquium/1886-colloquium-with-bowen-li-2025-10-22">https://www.sc.fsu.edu/news-and-events/colloquium/1886-colloquium-with-bowen-li-2025-10-22</a></div>
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