Convex clustering has drawn recent attention because of its competitive performance and nice property to guarantee global optimality. However, convex clustering is infeasible due to its high computational cost for large-scale data sets. We propose a novel method to solve the L1 fusion convex clustering problem by dynamic programming. We develop the Convex clustering Path Algorithm In Near-linear Time (C-PAINT) algorithm to construct the solution path efficiently. The proposed C-PAINT yields the exact solution while other general solvers for convex problems applied in the convex clustering depend on tuning parameters such as step size and threshold, and it usually takes many iterations to converge. Including a sorting process that almost takes no time in practice, the main part of the algorithm takes only linear time. Thus, C-PAINT has superior scalability comparing to other state-of-art algorithms. Moreover, C-PAINT enables the path visualization of clustering solutions for large data. In particular, experiments show our proposed method can solve the convex clustering with 10^7 data points in two minutes. We demonstrate the proposed method using both synthetic data and real data. Our algorithms are implemented in the dpcc R package.

Dynamic visualization for L1 fusion convex clustering in near-linear time

Lightning Talks II

lightning talk

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) is held fully online. We have an exciting program consisting of keynote talks by [Susan Murphy](https://www.auai.org/uai2021/program?block=A), [Eric Horvitz](https://www.auai.org/uai2021/program?block=B), [Lenka Zdeborová](https://www.auai.org/uai2021/program?block=C), [Judea Pearl](https://www.auai.org/uai2021/program?block=D) and [Ankur Moitra](https://www.auai.org/uai2021/program?block=E), a town hall meeting, peer-reviewed papers, and three workshops. The main conference is single-track and each paper is show-cased with a live component (either a live discussion and Q&A, or a live lightning talk). All papers are also featured in video presentations and in poster sessions in gather.town. The three parallel workshops on Friday allow for smaller meetings on current topics.

The walkthrough video below shows how you can navigate this site and use the different possibilities for interaction: UAIsland2021 on gather.town for posters and social interactions, the mementor platform for virtual mentoring sessions, and the chat and Q&A systems for discussions about talks and posters.

Have a great UAI 2021! 
<br>
<br>Cassio de Campos and Marloes Maathuis
<br>UAI 2021 Program Chairs
<br>uai2021programchairs@gmail.com

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A ticket is required to access this conference. Please use the provided link to register for the event.

https://auai.org/uai2021/registration

UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

Bingyuan Zhang

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Dynamic visualization for L1 fusion convex clustering in near-linear time

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