Search Results for "clustering algorithm matlab" - Page 2
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Showing 142 open source projects for "clustering algorithm matlab"
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MTCNN Face Detection Alignment
Joint Face Detection and Alignment
MTCNN_face_detection_alignment is an implementation of the “Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks” algorithm. The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage. The repository includes Caffe / MATLAB code, support scripts, and instructions for dependencies. ... -
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Blueprint XAS is a Matlab-based suite designed for the processing and analysis of near-edge x-ray absorption spectroscopy (XAS) data. The suite is designed primarily to assist users in exploring reasonable fit solutions while minimizing user bias.
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NaveGo
NaveGo: an open source MATLAB/GNU Octave toolbox for processing integr
NaveGo is an open source MATLAB/GNU Octave toolbox designed for processing integrated navigation systems, simulating inertial sensors and GNSS receivers, and profiling inertial sensors using methods like Allan variance—providing a community-driven simulation framework for navigation system design and analysis. I am reaching out to share an important update regarding the NaveGo project. Due to a shift in both my professional career and personal interests away from navigation systems, I have... -
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MScDB
A Mass Spectrometry Centric Protein Sequence Database for Proteomics
...The core modules of MScDB are an in-silico proteolytic digest and a peptide centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. -
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Hierarchical DP
Source code of DC-HDP
This Matlab code is used for demonstration of the clustering results of DP, LC-DP and DC-HDP. Written by Ye Zhu, Deakin University, May 2021, version 1.0. This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Zhu, Y., Ting, K.M., Jin, Y. and Angelova, M., 2021, August. -
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This Matlab code is used for demonstration of the effect of CDF-TS as a preprocessing method to transform data. Written by Ye Zhu, Deakin University, April 2021, version 1.0. This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Zhu, Y., Ting, K.M., Carman, M. and Angelova, M., 2021, April. CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern...
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens due to its broad range of activities and low toxicity. However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3... -
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yafu
Automated integer factorization
...The automation within YAFU is state-of-the-art, combining factorization algorithms in an intelligent and adaptive methodology that minimizes the time to find the factors of arbitrary input integers. Most algorithm implementations are multi-threaded, allowing YAFU to fully utilize multi- or many-core processors (including SNFS, GNFS, SIQS, and ECM). YAFU is primarily a command-line driven tool. You provide the number to factor and, via screen output and log files, YAFU will provide you the factors. There is also an interactive environment similar to MATLAB or PARI/GP, where you can type commands and store results. ... -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. -
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Isolation Similarity
aNNE similarity based on Isolation Kernel
Demo of using aNNE similarity for DBSCAN. Written by Xiaoyu Qin, Monash University, March 2019, version 1.0 This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Qin, X., Ting, K.M., Zhu, Y. and Lee, V.C., 2019, July. Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp.... -
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Smart Algorithm
Repository implementing a variety of intelligent algorithms
Smart-Algorithm is a repository implementing a variety of intelligent / metaheuristic optimization algorithms (e.g. Genetic Algorithm, Ant Colony, Particle Swarm, Immune Algorithm). The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. -
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SAR Synthetic Aperture Radar
Collection of MATLAB codes, simulations, and summaries
This repository is a collection of MATLAB codes, simulations, and summaries related to Synthetic Aperture Radar (SAR), InSAR, and PolSAR. The author compiled implementations of classic SAR imaging algorithms (e.g. Range-Doppler (RD) algorithm, Chirp Scaling (CS) algorithm), synthetic scenes, InSAR (interferometric) simulation including multiple terrain types (flat, conical), and polarization calibration techniques (Whitt, PARC, Quegan, Ainsworth). -
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Relative Overestimation VOP
VOP compression for MRI with overestimation control
...For better compression and speed use these: ***** https://sourceforge.net/projects/enhanced-sar-compression/ Virtual Observation Points SAR compression. Contains multiple Algorithms: Compression according to Eichfelder et al. and Lee et al. The Lee algorithm is enhanced by including overestimation control to reduce maximum relative overestimation. All algorithms are implemented in Matlab and were optimized for enhanced speed. Needs Matlab R2016b at least. Prefer R2018b or newer. ***** This work is outdated and is here for reference purpose, only. ***** If you are looking for improved SAR-compression algorithms, visit: https://sourceforge.net/projects/enhanced-sar-compression/ -
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A2RMS Algorithm
Implementation of the A2RMS Algorithm in Matlab
Implementation of the A2RMS Algorithm for univariate densities defined for real values. -
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LIPRAS, Line-Profile Analysis Software
Peak fitting GUI for diffraction data
LIPRAS v466 LIPRAS [LEEP-ruhs], short for Line-Profile Analysis Software, is a graphical user interface for least-squares fitting of Bragg peaks in powder diffraction data. For any region of the inputted data, user can choose which profile functions to apply to the fit, constrain profile functions, and view the resulting fit in terms of the profile functions chosen. If you use LIPRAS for your research, please cite it: Giovanni Esteves, Klarissa Ramos, Chris M. Fancher, and Jacob L.... -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes... -
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weka-MTreeClusterer
Flat clustering algorithm based on MTrees implemented for weka.
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DSeg software
A MATLAB program to segment filamentous bacteria and hyphae structures
...Here we present an image analysis tool DSeg to overcome the difficulties in finding complicated elongated cell shapes by using time-lapse data and cell morphological constraints. A fast binary level-set based algorithm is implemented for extracting object contour and refining shapes. The software is implemented in MATLAB for segmenting and tracking of cell contours from various microscopy systems. Video instructions: https://www.youtube.com/embed/qMbM0shkk7A?rel Lastest updates: 20190930 DSeg: A dynamic segmentation program to extract backbone patterns for filamentous bacteria and hyphae structures,(2018). ... -
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XBioSiP
RTL & Behavioral Models (Approx.) of Pan-Tompkins Application Stages
The "XBioSiP" library contains the RTL (VHDL) and behavioral (MATLAB) models of the approximate adders and multipliers used for designing approximate versions of the bio-signal processing Pan-Tompkins algorithm, including all of its application stages. This work was published in DAC 2019. In case of usage please refer to: B. S. Prabakaran, S. Rehman, M. Shafique, “XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge”, IEEE/ACM 56th Design Automation Conference (DAC), June 2019. -
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Clustering by Shared Subspaces
Grouping Points by Shared Subspaces for Effective Subspace Clustering
These functions implement a subspace clustering algorithm, proposed by Ye Zhu, Kai Ming Ting, and Mark J. Carman: "Grouping Points by Shared Subspaces for Effective Subspace Clustering", Published in Pattern Recognition Journal at https://doi.org/10.1016/j.patcog.2018.05.027 -
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De_Lux
Deconvolution of luminescence cross-talk in microplate reader data
An algorithm to deconvolve the luminescence cross-talk in high-throughput gene expression profiling to recover the true luminescence activity of a microplate. -
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Distance Scaling
A Distance Scaling Method to Improve Density-Based Clustering
These functions implement a distance scaling method, proposed by Ye Zhu, Kai Ming Ting, and Maia Angelova, "A Distance Scaling Method to Improve Density-Based Clustering", in PAKDD2018 proceedings: https://doi.org/10.1007/978-3-319-93040-4_31. -
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NNC
Nuclear Norm Clustering
We present Nuclear Norm Clustering (NNC), an algorithm that can be used in different fields as a promising alternative to the k-means clustering method, and that is less sensitive to outliers. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulate annealing techniques to choose an optimal L that minimizes NN(L). -
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MatlabFunc
Matlab codes for feature learning
MatlabFunc is a collection of MATLAB functions developed by the ZJULearning group to support various tasks in computer vision, machine learning, and numerical computation. The repository brings together a wide range of utility scripts, algorithms, and implementations that serve as building blocks for research and development. These functions cover areas such as matrix operations, optimization, data processing, and visualization, making them broadly applicable across different research...
