Publications associées à Algorithme de Lloyd-Max

Improving K-means Clustering Using Speculation

Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou, Stefan Igescu

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

2023

A finite element framework based on bivariate simplex splines on triangle configurations

Xiaodong Wei, Juan Cao

Recently, triangle configuration based bivariate simplex splines (referred to as TCB-spline) have been introduced to the geometric computing community. TCB-splines retain many attractive theoretic properties of classical B-splines, such as partition of uni ...

ELSEVIER SCIENCE SA2019

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Lingxiao Huang, Jian Li

We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...

IEEE COMPUTER SOC2018

Superpixels and Polygons using Simple Non-Iterative Clustering

Sabine Süsstrunk, Radhakrishna Achanta

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel bou ...

Ieee2017

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Nikolaos Geroliminis, Mohammadreza Saeedmanesh

Aggregated network level modeling and control of traffic in urban networks have recently gained a lot of interest due to unpredictability of travel behaviors and high complexity of physical modeling in microscopic level. Recent research has shown the exist ...

Elsevier2016

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Divesh Aggarwal

We give a randomized 2^{n+o(n)}-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic O(4^n)-time and O(2^n)-space algorithm of Miccian ...

ACM2015

Discovering places of interest in everyday life from smartphone data

Daniel Gatica-Perez

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. A place-of-interest is defined as a location where ...

Springer2013

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Pierre Vandergheynst, Alexandre Massoud Alahi, Laurent Jacques

In this paper, we propose to study the problem of localization of a dense set of people with a network of heterogeneous cameras. We propose to recast the problem as a linear inverse problem. The proposed framework is generic to any scene, scalable in the n ...

Springer Verlag2011

Interval consensus: from quantized gossip to voting

Martin Vetterli, Patrick Thiran, Florence Bénézit

We design distributed and quantized average consensus algorithms on arbitrary connected networks. By construction, quantized algorithms cannot produce a real, analog average. Instead, our algorithm reaches consensus on the quantized interval that contains ...

2009