List: PCA and Dimensionality Reduction Special Collection | Curated by Rukshan Pramoditha

Dec 22, 2023
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PCA and Dimensionality Reduction Special CollectionFind the ways (especially PCA) to reduce the size of your dataset while keeping as much of the variance as possible and see the amazing uses of dimensionality reduction!
Rukshan Pramoditha
t-SNE Visualization with Yellowbrick — A Fast and Easy Method
Jun 7, 2023
Jun 7, 2023
Rukshan Pramoditha
in
Towards Data Science
How t-SNE Outperforms PCA in Dimensionality ReductionPCA vs t-SNE for visualizing high-dimensional data in a lower-dimensional space
May 23, 2023
May 23, 2023
Rukshan Pramoditha
in
Towards Data Science
Non-Negative Matrix Factorization (NMF) for Dimensionality Reduction in Image DataDiscussing theory and implementation with Python and Scikit-learn
May 6, 2023
2
May 6, 2023
2
Rukshan Pramoditha
in
Towards Data Science
11 Dimensionality reduction techniques you should know in 2021Reduce the size of your dataset while keeping as much of the variation as possible
Apr 14, 2021
9
Apr 14, 2021
9
Rukshan Pramoditha
in
Towards Data Science
Singular Value Decomposition vs Eigendecomposition for Dimensionality ReductionPerforming PCA using both methods and comparing the results
Mar 20, 2023
1
Mar 20, 2023
1
Rukshan Pramoditha
in
Data Science 365
2 Plots That Help Me to Choose the Right Number of Principal ComponentsCreating the cumulative explained variance plot and the scree plot in PCA
Mar 12, 2023
1
Mar 12, 2023
1
Rukshan Pramoditha
in
Data Science 365
An In-depth Guide to PCA with NumPyThrough eigenvalue analysis
Feb 27, 2023
Feb 27, 2023
Rukshan Pramoditha
in
Data Science 365
Do we need feature scaling before Linear Discriminant Analysis (LDA)?LDA for dimensionality reduction with and without feature scaling
Jan 20, 2023
1
Jan 20, 2023
1
Rukshan Pramoditha
in
Towards Data Science
11 Different Uses of Dimensionality ReductionThe whole ML is full of dimensionality reduction and its applications. Let’s see them in action!
Dec 8, 2021
1
Dec 8, 2021
1
Rukshan Pramoditha
in
Towards Data Science
PCA vs Autoencoders for a Small Dataset in Dimensionality ReductionNeural Networks and Deep Learning Course: Part 45
Feb 16, 2023
1
Feb 16, 2023
1
Rukshan Pramoditha
in
Towards Data Science
Dimensionality Reduction for Linearly Inseparable DataNon-linear dimensionality reduction using kernel PCA
Dec 20, 2022
Dec 20, 2022
Rukshan Pramoditha
in
Data Science 365
3 Easy Steps to Perform Dimensionality Reduction Using Principal Component Analysis (PCA)Running the PCA algorithm twice is the most effective way of performing PCA
Jan 3, 2023
3
Jan 3, 2023
3
Rukshan Pramoditha
in
Towards Data Science
LDA Is More Effective than PCA for Dimensionality Reduction in Classification DatasetsLinear discriminant analysis (LDA) for dimensionality reduction while maximizing class separability
Dec 29, 2022
2
Dec 29, 2022
2
Rukshan Pramoditha
in
Towards Data Science
How to Select the Best Number of Principal Components for the DatasetSix methods you should follow
Apr 24, 2022
2
Apr 24, 2022
2
Rukshan Pramoditha
in
Towards Data Science
How Autoencoders Outperform PCA in Dimensionality ReductionDimensionality reduction with autoencoders using non-linear data
Aug 19, 2022
1
Aug 19, 2022
1
Rukshan Pramoditha
in
Towards Data Science
Using PCA to Reduce Number of Parameters in a Neural Network by 30x TimesWhile still getting even better performance! — Neural Networks and Deep Learning Course: Part 17
Jun 12, 2022
Jun 12, 2022
Rukshan Pramoditha
in
Towards Data Science
RGB Color Image Compression Using Principal Component AnalysisPCA in Action for Dimensionality Reduction
Mar 29, 2022
2
Mar 29, 2022
2
Rukshan Pramoditha
in
Data Science 365
Principal Component Analysis — 18 Questions AnsweredOne-stop place for your most of the questions regarding PCA
Mar 18, 2022
2
Mar 18, 2022
2
Rukshan Pramoditha
in
Data Science 365
Eigendecomposition of a Covariance Matrix with NumPyFor Principal Component Analysis (PCA)
Mar 13, 2022
Mar 13, 2022
Rukshan Pramoditha
in
Towards Data Science
How to Mitigate Overfitting with Dimensionality ReductionAddressing the problem of overfitting — Part 3
Sep 27, 2021
Sep 27, 2021