kernel-pca 开源项目推荐（53 个）

Albertsr /Anomaly-Detection

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

gionanide /Speech_Signal_Processing_and_Classification

前端语音处理旨在从语音话语的短期片段中提取适当的特征，这些片段称为框架。它是解决任何使用语音或音频（如音乐）模式识别问题的前提步骤。这里，我们关注声音障碍的分类。也就是说，开发两类分类器，能够区分例如声带麻痹患者和健康受试者的话语。人类语音产生系统的数学建模表明，全极系统功能是合理的[1-3]。因此，线性预测系数（LPC）成为建模语音短期频谱幅度的首选。LPC导出的中空系数保证区分系统（如声道）贡献与激发贡献。考虑到人耳特性，mel频率的中频频分相系数（MFCC）成为语音频谱包络的描述特征。与MFCCC类似，感知线性预测系数（PLP）也可以推导。上述说话传统特征将与由交集神经网络（CNN）（例如自编码器）提取的不可知性特征进行测试[4]。模式识别步骤将基于高斯混合模型分类器、K最近邻分类器、贝叶斯分类器以及深度神经网络。马萨诸塞州眼耳医院数据集（MEEI-Dataset）[5]将被利用。在应用层面，将开发一个用于Python特征提取和分类的库。可信的公开资源将被用于实现我们的目标，比如KALDI。将与[6-8]进行比较。

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.

bghojogh /Principal-Component-Analysis

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

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.

OleguerCanal /GPLVM

Re-Implementation of Gaussian Process Latent Variable Model algorithm & performance assessment against Kernel-PCA

kashefy /mi2notes

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

MaxenceGiraud /BayesianPCA

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

juyongjiang /VFedPCA-VFedAKPCA

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

fanghenshaometeor /ood-kernel-pca

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

namanUIUC /NonlinearComponentAnalysis

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

kyomangold /ETH-MachineLearning

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

tejasnp163 /Dimensionality-Reduction-on-Wine-Dataset

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

matteo-serafino /dimensionality-reduction-package

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

ratvec /ratvec

Low-dimensional vector representations via kernel PCA with rational kernels

FulgentMcGuffin /gulfstream

Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions

fanghenshaometeor /ood-kernel-pca-ext

[TPAMI 2026] Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation

bghojogh /Image-Structural-Component-Analysis

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

algostatml /UNSUPERVISED-ML

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