Difference between revisions of "Dimensional Reduction"
m (BPeat moved page Dimensional Reduction Algorithms to Dimensional Reduction without leaving a redirect) |
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Algorithms: | Algorithms: | ||
| − | * [[Principal Component Analysis (PCA)]] | + | ** [[Principal Component Analysis (PCA)]] |
| − | * [http://en.wikipedia.org/wiki/Independent_component_analysis[Independent Component Analysis (ICA)] | + | ** [http://en.wikipedia.org/wiki/Independent_component_analysis[Independent Component Analysis (ICA)] |
| − | * [http://en.wikipedia.org/wiki/Canonical_correlation Canonical Correlation Analysis (CCA)] | + | ** [http://en.wikipedia.org/wiki/Canonical_correlation Canonical Correlation Analysis (CCA)] |
| − | * [http://en.wikipedia.org/wiki/Linear_discriminant_analysis Linear Discriminant Analysis (LDA)] | + | ** [http://en.wikipedia.org/wiki/Linear_discriminant_analysis Linear Discriminant Analysis (LDA)] |
| − | * [http://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)] | + | ** [http://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)] |
| − | * [http://en.wikipedia.org/wiki/Non-negative_matrix_factorization Non-Negative Matrix Factorization (NMF)]] | + | ** [http://en.wikipedia.org/wiki/Non-negative_matrix_factorization Non-Negative Matrix Factorization (NMF)]] |
| − | * [http://en.wikipedia.org/wiki/Partial_least_squares_regression Partial Least Squares Regression (PLSR)] | + | ** [http://en.wikipedia.org/wiki/Partial_least_squares_regression Partial Least Squares Regression (PLSR)] |
| − | * [http://en.wikipedia.org/wiki/Principal_component_regression[Principal Component Regression (PCR)] | + | ** [http://en.wikipedia.org/wiki/Principal_component_regression[Principal Component Regression (PCR)] |
| − | * [http://en.wikipedia.org/wiki/Projection_pursuit Projection Pursuit] | + | ** [http://en.wikipedia.org/wiki/Projection_pursuit Projection Pursuit] |
| − | * [http://en.wikipedia.org/wiki/Sammon_mapping Sammon Mapping/Projection] | + | ** [http://en.wikipedia.org/wiki/Sammon_mapping Sammon Mapping/Projection] |
Revision as of 06:46, 5 April 2020
Youtube search... ...Google search
--> To identify the most important features
Algorithms:
- Principal Component Analysis (PCA)
- [Independent Component Analysis (ICA)
- Canonical Correlation Analysis (CCA)
- Linear Discriminant Analysis (LDA)
- Multidimensional Scaling (MDS)
- Non-Negative Matrix Factorization (NMF)]
- Partial Least Squares Regression (PLSR)
- [Principal Component Regression (PCR)
- Projection Pursuit
- Sammon Mapping/Projection
- Pooling / Sub-sampling: Max, Mean
- Kernel Trick
- Isomap
- Local Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Softmax
Related:
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Factor analysis
- Feature extraction
- Feature selection
- Seven Techniques for Dimensionality Reduction | KNIME
- Nonlinear dimensionality reduction | Wikipedia
Some datasets may contain many variables that may cause very hard to handle. Especially nowadays data collecting in systems occur at very detailed level due to the existence of more than enough resources. In such cases, the data sets may contain thousands of variables and most of them can be unnecessary as well. In this case, it is almost impossible to identify the variables which have the most impact on our prediction. Dimensional Reduction Algorithms are used in this kind of situations. It utilizes other algorithms like Random Forest, Decision Tree to identify the most important variables. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
