DSLib: An open source library for the dominant set clustering method
- URL: http://arxiv.org/abs/2010.07906v1
- Date: Thu, 15 Oct 2020 17:36:48 GMT
- Title: DSLib: An open source library for the dominant set clustering method
- Authors: Sebastiano Vascon, Samuel Rota Bul\`o, Vittorio Murino, Marcello
Pelillo
- Abstract summary: DSLib is an open-source implementation of the Dominant Set (DS) clustering algorithm written entirely in Matlab.
The DS method is a graph-based clustering technique rooted in the evolutionary game theory that starts gaining lots of interest in the computer science community.
- Score: 32.98501095385102
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: DSLib is an open-source implementation of the Dominant Set (DS) clustering
algorithm written entirely in Matlab. The DS method is a graph-based clustering
technique rooted in the evolutionary game theory that starts gaining lots of
interest in the computer science community. Thanks to its duality with game
theory and its strict relation to the notion of maximal clique, has been
explored in several directions not only related to clustering problems.
Applications in graph matching, segmentation, classification and medical
imaging are common in literature. This package provides an implementation of
the original DS clustering algorithm since no code has been officially released
yet, together with a still growing collection of methods and variants related
to it. Our library is integrable into a Matlab pipeline without dependencies,
it is simple to use and easily extendable for upcoming works. The latest source
code, the documentation and some examples can be downloaded from
https://xwasco.github.io/DominantSetLibrary.
Related papers
- Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification [0.4038539043067986]
cuvis.ai is an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training.
The package is written in Python and provides wrappers around common machine learning libraries.
arXiv Detail & Related papers (2024-11-18T06:33:40Z) - From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited [51.24526202984846]
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
graph convolutional networks (GCNs) have become the predominant techniques for their promising performance.
arXiv Detail & Related papers (2023-09-24T10:10:21Z) - SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks.
It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches.
We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
arXiv Detail & Related papers (2023-04-21T10:00:22Z) - HiPart: Hierarchical Divisive Clustering Toolbox [0.0]
HiPart is an open-source python library that provides efficient and interpret-able implementations of divisive hierarchical clustering algorithms.
HiPart supports interactive visualizations for the manipulation of the execution steps allowing the direct intervention of the clustering outcome.
arXiv Detail & Related papers (2022-09-18T23:48:43Z) - Systematically improving existing k-means initialization algorithms at
nearly no cost, by pairwise-nearest-neighbor smoothing [1.2570180539670577]
We present a meta-method for initializing the $k$-means clustering algorithm called PNN-smoothing.
It consists in splitting a given dataset into $J$ random subsets, clustering each of them individually, and merging the resulting clusterings with the pairwise-nearest-neighbor method.
arXiv Detail & Related papers (2022-02-08T15:56:30Z) - Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise
Learning [65.54757265434465]
Pairwise learning refers to learning tasks where the loss function depends on a pair instances.
Online descent (OGD) is a popular approach to handle streaming data in pairwise learning.
In this paper, we propose simple and online descent to methods for pairwise learning.
arXiv Detail & Related papers (2021-11-23T18:10:48Z) - Build your own tensor network library: DMRjulia I. Basic library for the
density matrix renormalization group [0.0]
The focus of this code is on basic operations involved in tensor network computations.
The code is fast enough to be used in research and can be used to make new algorithms.
arXiv Detail & Related papers (2021-09-07T14:31:47Z) - Efficient Graph Deep Learning in TensorFlow with tf_geometric [53.237754811019464]
We introduce tf_geometric, an efficient and friendly library for graph deep learning.
tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs.
The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc.
arXiv Detail & Related papers (2021-01-27T17:16:36Z) - Picasso: A Sparse Learning Library for High Dimensional Data Analysis in
R and Python [77.33905890197269]
We describe a new library which implements a unified pathwise coordinate optimization for a variety of sparse learning problems.
The library is coded in R++ and has user-friendly sparse experiments.
arXiv Detail & Related papers (2020-06-27T02:39:24Z) - Kernel methods library for pattern analysis and machine learning in
python [0.0]
The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion.
The library provides a number of well-defined classes to make various kernel-based operations efficient.
arXiv Detail & Related papers (2020-05-27T16:44:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.