Web: http://arxiv.org/abs/2209.08004

Sept. 19, 2022, 1:12 a.m. | Boris Landa, Xiuyuan Cheng

stat.ML updates on arXiv.org arxiv.org

The Gaussian kernel and its traditional normalizations (e.g., row-stochastic)
are popular approaches for assessing similarities between data points, commonly
used for manifold learning and clustering, as well as supervised and
semi-supervised learning on graphs. In many practical situations, the data can
be corrupted by noise that prohibits traditional affinity matrices from
correctly assessing similarities, especially if the noise magnitudes vary
considerably across the data, e.g., under heteroskedasticity or outliers. An
alternative approach that provides a more stable behavior under noise …

arxiv geometry inference manifold math scaling stochastic

More from arxiv.org / stat.ML updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Senior Data Engineer

@ HealthVerity | United States

Data Analyst - Business Insights

@ Sertis | Bangkok

Biomarker Data Management Specialist

@ Precision Medicine Group | Remote, United States