Publications related to Sublinear Algorithms for Spectral Graph Clustering

A Bi-Criteria Approximation Algorithm for k-Means

We consider the classical k-means clustering problem in the setting of bi-criteria approximation, in which an algorithm is allowed to output beta*k > k clusters, and must produce a clustering with cost at most alpha times the to the cost of the optimal set ...

Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik2016

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

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show l ...

EDM2016

Low-Rank Matrices on Graphs: Generalized Recovery & Applications

Pierre Vandergheynst, Nathanaël Perraudin, Nauman Shahid

Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geometry of the space, resulting in a highly under-determined recovery problem. ...

2016

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

Jean-Philippe Thiran, Devis Tuia, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

Clustering algorithms have evolved to handle more and more complex structures. However, measures allowing to qualify the quality of such partitions are rare and only specic to certain algorithms. In this work, we propose a new cluster validity measure (CVM ...

Elsevier2015

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Xin Guo

Non-negative matrix factorization (NMF) based sound source separation involves two phases: First, the signal spectrum is decomposed into components which, in a second step, are clustered in order to obtain estimates of the source signal spectra. The major ...

Ieee2015

Distributed Clustering and Learning Over Networks

Ali H. Sayed

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursu ...

IEEE2015

Adaptive clustering for multitask diffusion networks

Ali H. Sayed, Jie Chen

Diffusion LMS was originally conceived for online distributed parameter estimation in single-task environments where agents pursue a common objective. However, estimating distinct but correlated objects (multitask problems) is useful in many applications. ...

IEEE2015

Diffusion LMS Over Multitask Networks

Ali H. Sayed, Jie Chen

The diffusion LMS algorithm has been extensively studied in recent years. This efficient strategy allows to address distributed optimization problems over networks in the case where nodes have to collaboratively estimate a single parameter vector. Neverthe ...

Institute of Electrical and Electronics Engineers2015

Sublinear Algorithms for Spectral Graph Clustering

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A Bi-Criteria Approximation Algorithm for k-Means

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

Temporally Coherent Clustering of Student Data

Low-Rank Matrices on Graphs: Generalized Recovery & Applications

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Distributed Clustering and Learning Over Networks

Adaptive clustering for multitask diffusion networks

Diffusion LMS Over Multitask Networks

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Distributed Clustering and Learning Over Networks

Temporally Coherent Clustering of Student Data

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

Low-Rank Matrices on Graphs: Generalized Recovery & Applications

A Bi-Criteria Approximation Algorithm for k-Means

Adaptive clustering for multitask diffusion networks

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Diffusion LMS Over Multitask Networks