principal-component-analysis
There are 1113 repositories under principal-component-analysis topic.
nsoojin/coursera-ml-py
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
MaxHalford/prince
:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
abess-team/abess
Fast Best-Subset Selection Library
jayshah19949596/Machine-Learning-Models
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
erdogant/pca
pca: A Python Package for Principal Component Analysis.
Albertsr/Anomaly-Detection
UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.
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].
zavtech/morpheus-core
The foundational library of the Morpheus data science framework
arnaldog12/Machine_Learning
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
dlaptev/RobustPCA
Robust PCA implementation and examples (Matlab)
faridcher/ml-course
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
lestercardoz11/fault-detection-for-predictive-maintenance-in-industry-4.0
This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.
bwlewis/irlba
Fast truncated singular value decompositions
erichson/ristretto
Randomized Dimension Reduction Library
Hassaan-Elahi/Writing-Styles-Classification-Using-Stylometric-Analysis
✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
tiskw/random-fourier-features
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
erichson/rSVD
Randomized Matrix Decompositions using R
MaksimEkin/COVID19-Literature-Clustering
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.
hiroyuki-kasai/ClassifierToolbox
A MATLAB toolbox for classifier: Version 1.0.7
olivertomic/hoggorm
Explorative multivariate statistics in Python
davircarvalho/Individualized_HRTF_Synthesis
Synthesis of individualized HRTFs based on Neural Networks, Principal Component Analysis and anthropometry
scholi/pySPM
Python library to handle Scanning Probe Microscopy Images. Can read nanoscan .xml data, Bruker AFM images, Nanonis SXM files as well as iontof images(ITA, ITM and ITS).
AdiChat/Faces
Do you look like a Nobel Laureate :medal_military:, Physicist, Chemist, Mathematician, Actor or a Programmer? God gave you one face and you went on to get a peek into the mind of God. :cloud_with_lightning:
dmey/synthia
📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python
jonghough/jlearn
Machine Learning Library, written in J
ikergarcia1996/Handwritten-Names-Recognition
The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
nicrie/xmca
Maximum Covariance Analysis in Python
jbramburger/Data-Science-Methods
This repository contains lecture notes and codes for the course "Computational Methods for Data Science"
Parveshdhull/FaceRecognitionUsing-PCA-2D-PCA-And-2D-Square-PCA
Implementation of PCA/2D-PCA/2D(Square)-PCA in Python for recognizing Faces: 1. Single Person Image 2. Group Image 3. Recognize Face In Video
Shikhargupta/Machine-Learning-and-Pattern-Recognition
Implementation of Machine Learning Algorithms
zhaoyichanghong/machine_learing_algo_python
implement the machine learning algorithms by python for studying
Piyush-Bhardwaj/Breast-cancer-diagnosis-using-Machine-Learning
Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.
benedekrozemberczki/NMFADMM
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
tirthajyoti/R-stats-machine-learning
Misc Statistics and Machine Learning codes in R
aedin/PCAworkshop
An introduction to matrix factorization and PCA and SVD.
pbloem/pca-book
Source files for a book on Principal component analysis