kernel-pca
There are 43 repositories under kernel-pca topic.
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].
Nikronic/Machine-Learning-Models
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
kaur-anupreet/Software-Defect-Prediction
Application of Deep Learning and Feature Extraction in Software Defect Prediction
lucko515/dataset-dimensionality-reduction-python
Here I've demonstrated how and why should we use PCA, KernelPCA, LDA and t-SNE for dimensionality reduction when we work with higher dimensional datasets.
bghojogh/Principal-Component-Analysis
The code for Principal Component Analysis (PCA), dual PCA, Kernel PCA, Supervised PCA (SPCA), dual SPCA, and Kernel SPCA
kashefy/mi2notes
My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin
mlesnoff/rchemo
Archived repo (see Readme) - R package for regression and discrimination, with special focus on chemometrics and high-dimensional data.
mlesnoff/rnirs
Archived repo - This R Package is not developed anymore (only maintenance). It was replaced by R package rchemo
OleguerCanal/GPLVM
Re-Implementation of GPLVM algorithm & performance assessment against Kernel-PCA
juyongjiang/VFedPCA-VFedAKPCA
Source Code & Datasets for "Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data"
MaxenceGiraud/BayesianPCA
Implementation of Bayesian PCA [Bishop][1999] And Bayesian Kernel PCA
namanUIUC/NonlinearComponentAnalysis
Application of principal component analysis capturing non-linearity in the data using kernel approach
tejasnp163/Dimensionality-Reduction-on-Wine-Dataset
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
ratvec/ratvec
Low-dimensional vector representations via kernel PCA with rational kernels
bghojogh/Image-Structural-Component-Analysis
The code for Image Structural Component Analysis (ISCA) and Kernel ISCA
matteo-serafino/dimensionality-reduction-package
Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.
AAU-Dat/P5-Nonlinear-Dimensionality-Reduction
5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.
Arminkhayati/Machine_learning_lec
My Machine Learning course projects
charumakhijani/machine-learning
Data Science Portfolio
hellpanderrr/ml_algorithms
Machine learning algorithms done from scratch in Python with Numpy/Scipy
kyomangold/ETH-MachineLearning
Repository for the code of the "Introduction to Machine Learning" (IML) lecture at the "Learning & Adaptive Systems Group" at ETH Zurich.
algostatml/UNSUPERVISED-ML
Unsupervised machine learning algorithm. Classical and kernel methods for non-linearly seperable data.
EthanJamesLew/PSU_STAT672
Notes, homework and project for PSU's STAT 672 Winter 2020
Joycechidi/MachineLearning2
Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance
longhongc/CMSC828C-hw6
K-means, Spectral clustering, PCA, and Kernel PCA
adelelwan24/Credit-Card-Customer-Segmentation
This repository implements customer segmentation techniques to analyze credit card user behavior and identify distinct customer groups. By leveraging Python libraries like pandas, Scipy and scikit-learn.
BeataWereszczynska/UML_forged_banknotes
UML dimensionality reduction and clustering models for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)
kisoo95/DACON-Monthly-DACON-Credit-Card-Fraud-Deal-Detection-AI-Competition
Winning one of the DACON competition
mehdi-aitaryane/PCA-Algorithms-From-Scratch-With-Examples
Implementation of PCA and Kernel PCA algorithms from scratch with practical examples, including datasets and image processing tasks like compression and denoising.
mohammad95labbaf/UMAP_breast_cancer
This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.
shubhams821/ML-Repository
Machine Learning assignments from coursework.
williamagyapong/data-mining-projects
Houses a series of projects I worked on for a course in Data Mining that I took in my Ph.D. Data Science program at UTEP in the Fall of 2022. Covers areas such as Regularized Logistic Regression, Optimization, Kernel Methods, PageRank, Kernel PCA, Association Rule Mining, Anomaly Detection, Parametric/Nonparametric Nonlinear Regression, etc.
alessimichele/Unsupervised-Learning-2023
This repository is dedicated to the lab activities of the course of Unsupervised Learning @UniTs
FabrizioMusacchio/machine_learning_for_image_denoising
This repository contains the Python code my blog post Image denoising techniques: A comparison of PCA, kernel PCA, autoencoder, and CNN. See post for more details and results.
Mehrab-Kalantari/News-Clustering
Applying NLP methods and kernel PCA on news dataset to build a clustering model