dimensionality-reduction-technique | Topic

Topic: "dimensionality-reduction-technique"

Ayantika22/PCA-Principle-Component-Analysis-For-Seed-Dataset

PCA(Principle Component Analysis) For Seed Dataset in Machine Learning

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Ayantika22/LDA-Linear-Discriminant-Analysis-for-Seed-Dataset

LDA(Linear Discriminant Analysis) for Seed Dataset

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SahilK-027/Factor-Analysis-Mini-Project

Mini project on Dimensionality Reduction

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rajvi-patel-22/Linear-Discriminant-Analysis-of-a-32x32-grayscale-image-for-image-compression.

Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (curse of dimensionality) and also reduce computational costs. Pertaining to our problem, we are given a [32 X 32] binary image as input and the goal is to apply LDA technique to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability between two classes in our case with the minimal loss.

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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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