[Forum SIS] Webinar DIDONG LI

[Matteo Ruggiero]

WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO <https://www.carloalberto.org/events/category/seminars/seminars-in-statistics/>

Joint initiative with 

MIDAS COMPLEX MODELING RESEARCH NETWORK <http://midas.mat.uc.cl/network>


Venerdi 20 Novembre 2020, alle ore 17:00, si terrà il seguente webinar:

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Speaker: Didong Li (Princeton and UCLA)

Title: Learning & Exploiting Low-Dimensional Structure in High-Dimensional Data

Abstract: 
Data lying in a high-dimensional ambient space are commonly thought to have a much lower intrinsic dimension. In particular, the data may be concentrated near a lower dimensional subspace or manifold. There is an immense literature focused on approximating the unknown subspace and the unknown density, and exploiting such approximations in clustering, data compression, and building of predictive models. Most of the literature relies on approximating subspaces and densities using a locally linear, and potentially multi-scale, dictionary with Gaussian kernels. In this talk, we propose a simple and general alternative, which instead uses pieces of spheres, or spherelets, to locally approximate the unknown subspace. I will also introduce a curved kernel called the the Fisher–Gaussian (FG) kernel which outperforms multivariate Gaussians in many cases. Theory is developed showing that spherelets can produce lower covering numbers and mean square errors for many manifolds, as well as the posterior consistency of the Dirichlet process mixture of FG kernels. Results relative to state-of-the-art competitors show gains in ability to accurately approximate the subspace and the density with fewer components and parameters. Time permitting, I will also present some applications of spherelets, including classification, geodesic distance estimation and clustering.
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Join Zoom Meeting
https://us02web.zoom.us/j/81832398725?pwd=QUl5eXNIM0xFSXNjTG9IaFZkL0lCQT09 <https://www.google.com/url?q=https%3A%2F%2Fus02web.zoom.us%2Fj%2F81832398725%3Fpwd%3DQUl5eXNIM0xFSXNjTG9IaFZkL0lCQT09&sa=D&ust=1604239536792000&usg=AOvVaw2gzlThyIXd4T0ym417GQXf>
Meeting ID: 818 3239 8725
Passcode: 768768


Il webinar è organizzato dalla "de Castro" Statistics Initiative 
www.carloalberto.org/stats <http://www.carloalberto.org/stats> 
nell’ambito del Complex Data Modeling Research Network 
midas.mat.uc.cl/network <http://midas.mat.uc.cl/network>
in collaborazione con il Collegio Carlo Alberto.


Cordiali saluti
Matteo Ruggiero

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Matteo Ruggiero
University of Torino and Collegio Carlo Alberto
www.matteoruggiero.it



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