spectral-clustering
There are 195 repositories under spectral-clustering topic.
kzampog/cilantro
A lean C++ library for working with point cloud data
shobrook/communities
Library of graph clustering algorithms
wq2012/SpectralCluster
Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.
FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
Reproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling".
gionanide/Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
lucianolorenti/SpectralClustering.jl
Spectral clustering algorithms written in Julia
zhaoyichanghong/machine_learing_algo_python
implement the machine learning algorithms by python for studying
Dref360/spectral-metric
Code for the CVPR 2019 paper : Spectral Metric for Dataset Complexity Assessment
Jonas1312/community-detection-in-graphs
Community Detection in Graphs (master's degree short project)
FilippoMB/Simplifying-Clustering-with-Graph-Neural-Networks
Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neural Networks".
mondalanindya/ICCVW2021_GSCEventMOD
Moving Object Detection for Event-based vision using Graph Spectral Clustering (Python implementation)
virelay/corelay
CoRelAy is a tool to compose small-scale (single-machine) analysis pipelines.
abojchevski/rsc
Robust Spectral Clustering. Implementation of "Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings".
waynezhanghk/gactoolbox
Graph Agglomerative Clustering (GAC) toolbox
ponimatkin/ssl-vos
[WACV 2023] A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
huangdonghere/USPEC_USENC
TKDE 2020: Ultra-Scalable Spectral Clustering and Ensemble Clustering (U-SPEC & U-SENC) #large-scale spectral clustering# #large-scale ensemble clustering#
Li-Hongmin/MyPaperWithCode
A simple implementation of our paper
waynezhanghk/gacluster
Graph Agglomerative Clustering Library
youweiliang/ConsistentGraphLearning
MATLAB code for the ICDM paper "Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering"
FilippoMB/Total-variation-graph-neural-networks
Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.
cshaowang/pic
Spectral Perturbation Meets Incomplete Multi-view Data
PranavPutsa1006/Speaker-Diarization
Identifying individual speakers in an audio stream based on the unique characteristics found in individual voices using Python
crisbodnar/regularised-spectral-clustering
Python code for reproducing the results of Understanding Regularized Spectral Clustering via Graph Conductance
microsoft/ISLE
This repository provides code for SVD and Importance sampling-based algorithms for large scale topic modeling.
saman-nia/Deep-Neural-Networks-for-Clustering
Deep Learning Clustering with Tensor-Flow in Python
neonwatty/spectral-clustering-demo
A fun review of spectral clustering with MATLAB demos I made for the NU machine learning meetiup in 2014
zhangyk8/Spectral-Clustering
Python3 implementation of the normalized and unnormalized spectral clustering algorithms
imtiazziko/Variational-Fair-Clustering
Variational Fair clustering
salimandre/graph-clustering
unsupervised clustering, generative model, mixed membership stochastic block model, kmeans, spectral clustering, point cloud data
bo1929/MuDCoD
MuDCoD: Multi-subject Dynamic Community Detection
UCD4IDS/MultiscaleGraphSignalTransforms.jl
MultiscaleGraphSignalTransforms.jl is a collection of software tools written in the Julia programming language for graph signal processing including HGLET, GHWT, eGHWT, NGWP, Lapped NGWP, and Lapped HGLET. Some of them were originally written in MATLAB by Jeff Irion, but we added more functionalities, e.g., eGHWT, NGWP, etc.
crj32/Spectrum
Density adaptive spectral clustering for single or multi-view data
JoshuaDBruton/SparseCoefficientClustering
Spectral Clustering on the Sparse Coefficients of Learned Dictionaries - Published in SIVP
fork123aniket/Graph-Clustering-using-Graph-Neural-Networks-from-scratch
Implementation of Graph pooling and clustering operation using Graph Neural Networks in PyTorch
jia-yi-chen/Social-Media-Mining
Python Implementation of algorithms in Social Media Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.
yuzhenfeng2002/Predicting-for-Each-Dynamic-Ridepooling-Order-on-Grid-Network
Codes in this repository are aimed to implement the prediction & simulation of the mathematical model in the paper [https://doi.org/10.1016/j.trb.2021.10.005] on a grid network and try to divide ODs into several clusters to accelerate the process.