Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

常用降维算法的复现和比较，包括LDA, PCA, KPCA, MDS, Isomap, LLE。

Kernel PCA for Novelty Detection

Kernel PCA and Pre-Image Reconstruction

Reproduction of the experiments presented in Kernel PCA and De-noising in Feature Spaces, as a project in DD2434 Machine Learning Advance Course during Winter 2016

Illustrate and code both PCA and KPCA.

Anomaly detection on a production line using principal component analysis (PCA) and kernel principal component analysis (KPCA) *from scratch*.

A Matlab Implementation of Kernel PCA. Specific example, with a Gaussian Kernel function. Data is provided as a mat file.

Application of PCA and KPCA algorithms to perform dimensionality reduction on the set of parameters in LPV models

PCAnet and different PCA methods, implemented with numpy. Including powerPCA, HebbianPCA, kernelPCA and PCAnet

kPCA-RNN model for forecasting DO concentration

• Partition trading time series data into 30 minutes intervals by picking the mean transaction price and volumes in each interval and compute the log-return (aka ’U sequence’) and write it into a corresponding csv file: JNJ_1004_1015_2010_HFT_30min_.csv • Visualize the high frequency data with PCA by using 2 or 3 PCs: you need to calculate the variance explained ratios for your visualization. • Identify outliers in your PCA analysis • Visualize it by using KPCA and compare its results with those of PCA (you need to at least try two kernels)

用PCA/KPCA实现人脸识别

several spectral-based matrix decomposition techniques, coded in Python/NumPy, for segmentation/clustering, dimension reduction

5/4/2018 DSP mini project that provides the description of dimension reduction method; namely Kernel Principal Component Analysis (KPCA) as data mining is concerned with finding meaningful patterns in large sets of data. It considers other techniques that are used in the application of the method namely the centering in feature space. And the numerical experiments that were performed on an iris dataset. Kernel seeks to project the set of data onto a low-dimensional subspace that captures the highest possible amount of variance in the data. Kernel PCA embeds the data into a high dimensional space, called the feature space. The project reduces the time and storage space required, improves the performance of the machine learning model and reduces the computational power and it becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.

Facial analysis using PCA and KPCA

Advanced Techniques in Face Recognition!

Machine learning code with examples in Perceptron, Logic Regression, SVMs, KNNs, PCA, LDA, KPCA, Ensemble Learning, ANNs, CNNs, and DCNN.

In this repository it presents Scikit-learn libraries for the development of Machine Learning in Python so that they can deepen or incorporate into their projects, within which I describe features of the models: PCA and IPCA, Kernels and KPCA, regularization through Lasso and Ridge , ElasticNet, outlier resolution, data preparation for robust regressions, assembly methods, and data preparation for these methods, Bagging, Boosting implementation, clustering strategies, Batch K-Means and Mean-Shhift, Cross Validation, and use K-Folds, paramedical optimization, implementation of Randomized, exit to production with API in Flask If you have any questions and / or comments, you can contact me at my email: gonzalo-a@hotmail.com Thank you

PCA, LDA, KPCA

PCA, Approximate PCA, Kernel PCA, Approximate KPCA

kernel PCA implementation

Dimensionality Reduction

No description available

This repository contains both Principal Component Analysis (PCA) and Kernel - PCA (KPCA)

Graduation Admission Acceptance Prediction Using PCA, KPCA and logistic regression

Feature Extraction Methods for LA Ozone and CO2 Data

Used Kernel PCA to extract the principle components of non-linearly separable dataset of SUV Buyers. Modeled Logistic Regression to classify whether a person will buy a SUV or not. Model Accuracy is 91.25%

Classifying images from Yale database using the following 6 techniques : (1)pca, (2)kpca, (3)lda, (4)kernel fisher face, (5)VGGFace, (6)ResNet

Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA)