Difference between revisions of "Dimensional Reduction"
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* [[Softmax]] | * [[Softmax]] | ||
* [[Local Linear Embedding (LLE) | Embedding functions]] | * [[Local Linear Embedding (LLE) | Embedding functions]] | ||
| − | * [http://github.com/JonTupitza/Data-Science-Process/blob/master/06-Dimensionality-Reduction.ipynb Dimensionality Reduction Techniques Jupyter Notebook | | + | * [http://github.com/JonTupitza/Data-Science-Process/blob/master/06-Dimensionality-Reduction.ipynb Dimensionality Reduction Techniques Jupyter Notebook] | [http://github.com/jontupitza Jon Tupitza] |
| Line 27: | Line 27: | ||
* Algorithms: | * Algorithms: | ||
** [[Principal Component Analysis (PCA)]] | ** [[Principal Component Analysis (PCA)]] | ||
| − | ** [http://en.wikipedia.org/wiki/Independent_component_analysis | + | ** [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)] | ||
| Line 33: | Line 33: | ||
** [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 | + | ** [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 07:05, 5 April 2020
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- Pooling / Sub-sampling: Max, Mean
- Kernel Trick
- Isomap
- Local Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Softmax
- Embedding functions
- Dimensionality Reduction Techniques Jupyter Notebook | Jon Tupitza
To identify the most important Features to address:
- computing
- 2D & 3D intuition often fails in higher dimensions
- distances tend to become relatively the 'same' as the number of dimensions increases
- 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
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
