Javdat/RcppKPCA

A computationally efficient implementation of Kernel Principal Component Analysis (KPCA) using the Gaussian (RBF) kernel. This package leverages Rcpp and the Armadillo C++ library for fast matrix operations.

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fanghenshaometeor/ood-kernel-pca

[NeurIPS 2024] Kernel PCA for Out-of-Distribution Detection

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matteo-serafino/dimensionality-reduction-package

Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.

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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.

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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.

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OleguerCanal/GPLVM

Re-Implementation of GPLVM algorithm & performance assessment against Kernel-PCA

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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.

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alessimichele/Unsupervised-Learning-2023 Fork of ilariavascotto/Unsupervised-Learning-2023

This repository is dedicated to the lab activities of the course of Unsupervised Learning @UniTs

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kisoo95/DACON-Monthly-DACON-Credit-Card-Fraud-Deal-Detection-AI-Competition

Winning one of the DACON competition

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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.

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kyomangold/ETH-MachineLearning

Repository for the code of the "Introduction to Machine Learning" (IML) lecture at the "Learning & Adaptive Systems Group" at ETH Zurich.

Language: Python - Size: 568 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

bghojogh/Principal-Component-Analysis

The code for Principal Component Analysis (PCA), dual PCA, Kernel PCA, Supervised PCA (SPCA), dual SPCA, and Kernel SPCA

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juyongjiang/VFedPCA-VFedAKPCA

Source Code & Datasets for "Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data"

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Albertsr/Anomaly-Detection

UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

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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.

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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].

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Mehrab-Kalantari/News-Clustering

Applying NLP methods and kernel PCA on news dataset to build a clustering model

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hellpanderrr/ml_algorithms

Machine learning algorithms done from scratch in Python with Numpy/Scipy

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shubhams821/ML-Repository

Machine Learning assignments from coursework.

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algostatml/UNSUPERVISED-ML

Unsupervised machine learning algorithm. Classical and kernel methods for non-linearly seperable data.

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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.

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SarCode/ML-Code-Tutorials-Udemy

Complete Tutorial Guide with Code for learning ML

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longhongc/CMSC828C-hw6

K-means, Spectral clustering, PCA, and Kernel PCA

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kashefy/mi2notes

My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin

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AAU-Dat/P5-Nonlinear-Dimensionality-Reduction

5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.

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bghojogh/Image-Structural-Component-Analysis

The code for Image Structural Component Analysis (ISCA) and Kernel ISCA

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MaxenceGiraud/BayesianPCA

Implementation of Bayesian PCA [Bishop][1999] And Bayesian Kernel PCA

Language: Python - Size: 470 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 5 - Forks: 0

mlesnoff/rchemo

R package for regression and discrimination, with special focus on chemometrics and high-dimensional data (This package is only maintained. The new current package is "Jchemo", under construction in Julia language)

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namanUIUC/NonlinearComponentAnalysis

Application of principal component analysis capturing non-linearity in the data using kernel approach

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Arminkhayati/Machine_learning_lec

My Machine Learning course projects

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tejasnp163/Dimensionality-Reduction-on-Wine-Dataset

Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.

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mlesnoff/rnirs

R Package not developed anymore (only maintenance). Replaced by package rchemo

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Abhishek-Nalawade/Kernel-PCA

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ratvec/ratvec

Low-dimensional vector representations via kernel PCA with rational kernels

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vitthal-bhandari/High-Dimensional-Data-and-Gradient-Descent

Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R

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charumakhijani/machine-learning

Data Science Portfolio

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EthanJamesLew/PSU_STAT672

Notes, homework and project for PSU's STAT 672 Winter 2020

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afiliot/Non-Linear-Dimensionality-Reduction

Project on Non-Linear Dimensionality Reduction - ENSAE ParisTech

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Zhenye-Na/npca

📃 Exploration of Nonlinear Component Analysis as a Kernel Eigenvalue Problem

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sidharththapar/Machine-Learning-from-scratch

Implementation of supervised and unsupervised Machine Learning algorithms in python from scratch!

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