Fast PCA in 1-D Wasserstein Spaces via B-splines Representation and Metric Projection

التفاصيل البيبلوغرافية
العنوان: Fast PCA in 1-D Wasserstein Spaces via B-splines Representation and Metric Projection
المؤلفون: Matteo Pegoraro, Mario Beraha
المصدر: Proceedings of the AAAI Conference on Artificial Intelligence. 35:9342-9349
بيانات النشر: Association for the Advancement of Artificial Intelligence (AAAI), 2021.
سنة النشر: 2021
مصطلحات موضوعية: General Medicine
الوصف: We address the problem of performing Principal Component Analysis over a family of probability measures on the real line, using the Wasserstein geometry. We present a novel representation of the 2-Wasserstein space, based on a well known isometric bijection and a B-spline expansion. Thanks to this representation, we are able to reinterpret previous work and derive more efficient optimization routines for existing approaches. As shown in our simulations, the solution of these optimization problems can be costly in practice and thus pose a limit to their usage. We propose a novel definition of Principal Component Analysis in the Wasserstein space that, when used in combination with the B-spline representation, yields a straightforward optimization problem that is extremely fast to compute. Through extensive simulation studies, we show how our PCA performs similarly to the ones already proposed in the literature while retaining a much smaller computational cost. We apply our method to a real dataset of mortality rates due to Covid-19 in the US, concluding that our analyses are consistent with the current scientific consensus on the disease.
تدمد: 2374-3468
2159-5399
DOI: 10.1609/aaai.v35i10.17126
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::b2e060cc2cb40719d3b875aaa12c6078
https://doi.org/10.1609/aaai.v35i10.17126
رقم الانضمام: edsair.doi...........b2e060cc2cb40719d3b875aaa12c6078
قاعدة البيانات: OpenAIRE
الوصف
تدمد:23743468
21595399
DOI:10.1609/aaai.v35i10.17126