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Some thing interesting about kernel-pca
Some thing interesting about kernel-pca
kernel-pca,5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.
Organization: aau-dat
kernel-pca,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.
User: adelelwan24
kernel-pca,Project on Non-Linear Dimensionality Reduction - ENSAE ParisTech
User: afiliot
kernel-pca,UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.
User: albertsr
Home Page: https://github.com/Albertsr/Anomaly-Detection
kernel-pca,Unsupervised machine learning algorithm. Classical and kernel methods for non-linearly seperable data.
Organization: algostatml
kernel-pca,My Machine Learning course projects
User: arminkhayati
kernel-pca,Subnational Cholera Analysis in Yemen
User: asai2019
kernel-pca,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)
User: beatawereszczynska
kernel-pca,The code for Image Structural Component Analysis (ISCA) and Kernel ISCA
User: bghojogh
Home Page: https://link.springer.com/chapter/10.1007/978-3-030-27202-9_7
kernel-pca,The code for Principal Component Analysis (PCA), dual PCA, Kernel PCA, Supervised PCA (SPCA), dual SPCA, and Kernel SPCA
User: bghojogh
Home Page: https://arxiv.org/abs/1906.03148
kernel-pca,Data Science Portfolio
User: charumakhijani
kernel-pca,Notes, homework and project for PSU's STAT 672 Winter 2020
User: ethanjameslew
kernel-pca,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.
User: fabriziomusacchio
Home Page: https://www.fabriziomusacchio.com/blog/2023-06-20-ai_image_denoising/
kernel-pca,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].
User: gionanide
kernel-pca,Machine learning algorithms done from scratch in Python with Numpy/Scipy
User: hellpanderrr
kernel-pca,Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance
User: joycechidi
kernel-pca,Source Code & Datasets for "Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data"
User: juyongjiang
kernel-pca,My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin
User: kashefy
Home Page: https://www.ni.tu-berlin.de/menue/teaching_activities/all_courses/machine_intelligence_ii/
kernel-pca,Application of Deep Learning and Feature Extraction in Software Defect Prediction
User: kaur-anupreet
kernel-pca,Winning one of the DACON competition
User: kisoo95
Home Page: https://dacon.io/competitions/official/235930/codeshare/6002
kernel-pca,Repository for the code of the "Introduction to Machine Learning" (IML) lecture at the "Learning & Adaptive Systems Group" at ETH Zurich.
User: kyomangold
kernel-pca,K-means, Spectral clustering, PCA, and Kernel PCA
User: longhongc
kernel-pca,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.
User: lucko515
kernel-pca,Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.
User: matteo-serafino
kernel-pca,Implementation of Bayesian PCA [Bishop][1999] And Bayesian Kernel PCA
User: maxencegiraud
kernel-pca,Applying NLP methods and kernel PCA on news dataset to build a clustering model
User: mehrabkalantary
kernel-pca,Archived repo (see Readme) - R package for regression and discrimination, with special focus on chemometrics and high-dimensional data.
User: mlesnoff
kernel-pca,Archived repo - This R Package is not developed anymore (only maintenance). It was replaced by R package rchemo
User: mlesnoff
kernel-pca,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.
User: mohammad95labbaf
kernel-pca,Application of principal component analysis capturing non-linearity in the data using kernel approach
User: namanuiuc
kernel-pca,In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
User: nikronic
kernel-pca,Re-Implementation of GPLVM algorithm & performance assessment against Kernel-PCA
User: oleguercanal
kernel-pca,Low-dimensional vector representations via kernel PCA with rational kernels
Organization: ratvec
kernel-pca,Complete Tutorial Guide with Code for learning ML
User: sarcode
kernel-pca,Machine Learning assignments from coursework.
User: shubhams821
kernel-pca,Implementation of supervised and unsupervised Machine Learning algorithms in python from scratch!
User: sidharththapar
kernel-pca,Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
User: tejasnp163
kernel-pca,Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R
User: vitthal-bhandari
kernel-pca,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.
User: williamagyapong
kernel-pca,📃 Exploration of Nonlinear Component Analysis as a Kernel Eigenvalue Problem
User: zhenye-na
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