Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Meila, Marina"'
Autor:
Meilă, Marina, Zhang, Hanyu
Manifold learning (ML), known also as non-linear dimension reduction, is a set of methods to find the low dimensional structure of data. Dimension reduction for large, high dimensional data is not merely a way to reduce the data; the new representati
Externí odkaz:
http://arxiv.org/abs/2311.03757
We propose a paradigm for interpretable Manifold Learning for scientific data analysis, whereby we parametrize a manifold with $d$ smooth functions from a scientist-provided dictionary of meaningful, domain-related functions. When such a parametrizat
Externí odkaz:
http://arxiv.org/abs/2302.00263
Autor:
Zhang, Hanyu, Meila, Marina
We quantify the parameter stability of a spherical Gaussian Mixture Model (sGMM) under small perturbations in distribution space. Namely, we derive the first explicit bound to show that for a mixture of spherical Gaussian $P$ (sGMM) in a pre-defined
Externí odkaz:
http://arxiv.org/abs/2302.00242
Autor:
Evangelou, Nikolaos, Dietrich, Felix, Chiavazzo, Eliodoro, Lehmberg, Daniel, Meila, Marina, Kevrekidis, Ioannis G.
We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on t
Externí odkaz:
http://arxiv.org/abs/2204.12536
Autor:
Rastogi, Charvi, Stelmakh, Ivan, Shen, Xinwei, Meila, Marina, Echenique, Federico, Chawla, Shuchi, Shah, Nihar B.
Double-blind conferences have engaged in debates over whether to allow authors to post their papers online on arXiv or elsewhere during the review process. Independently, some authors of research papers face the dilemma of whether to put their papers
Externí odkaz:
http://arxiv.org/abs/2203.17259
Autor:
Meilă, Marina, Zhang, Hanyu
We address the problem of validating the ouput of clustering algorithms. Given data $\mathcal{D}$ and a partition $\mathcal{C}$ of these data into $K$ clusters, when can we say that the clusters obtained are correct or meaningful for the data? This p
Externí odkaz:
http://arxiv.org/abs/2107.14442
Autor:
Chen, Yu-Chia, Meilă, Marina
The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclos
Externí odkaz:
http://arxiv.org/abs/2107.10970
Autor:
Wan, Yali, Meila, Marina
Finding communities in networks is a problem that remains difficult, in spite of the amount of attention it has recently received. The Stochastic Block-Model (SBM) is a generative model for graphs with "communities" for which, because of its simplici
Externí odkaz:
http://arxiv.org/abs/2104.10347
The manifold Helmholtzian (1-Laplacian) operator $\Delta_1$ elegantly generalizes the Laplace-Beltrami operator to vector fields on a manifold $\mathcal M$. In this work, we propose the estimation of the manifold Helmholtzian from point cloud data by
Externí odkaz:
http://arxiv.org/abs/2103.07626
Autor:
Meila, Marina
Meila (2018) introduces an optimization based method called the Sublevel Set method, to guarantee that a clustering is nearly optimal and "approximately correct" without relying on any assumptions about the distribution that generated the data. This
Externí odkaz:
http://arxiv.org/abs/2006.10274