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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Updated
Jul 24, 2017 - Jupyter Notebook
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.
Implementation of supervised and unsupervised Machine Learning algorithms in python from scratch!
Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance
Machine learning algorithms done from scratch in Python with Numpy/Scipy
Application of principal component analysis capturing non-linearity in the data using kernel approach
📃 Exploration of Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Subnational Cholera Analysis in Yemen
Low-dimensional vector representations via kernel PCA with rational kernels
Data Science Portfolio
Notes, homework and project for PSU's STAT 672 Winter 2020
Application of Deep Learning and Feature Extraction in Software Defect Prediction
The code for Image Structural Component Analysis (ISCA) and Kernel ISCA
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
Implementation of Bayesian PCA [Bishop][1999] And Bayesian Kernel PCA
UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.
Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R
My Machine Learning course projects
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Source Code & Datasets for "Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data"
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