Difference between revisions of "Dimensional Reduction"
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* [[Kernel Trick]] | * [[Kernel Trick]] | ||
* [[Isomap]] | * [[Isomap]] | ||
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* [[Softmax]] | * [[Softmax]] | ||
* [http://github.com/JonTupitza/Data-Science-Process/blob/master/06-Dimensionality-Reduction.ipynb Dimensionality Reduction Techniques Jupyter Notebook] | [http://github.com/jontupitza Jon Tupitza] | * [http://github.com/JonTupitza/Data-Science-Process/blob/master/06-Dimensionality-Reduction.ipynb Dimensionality Reduction Techniques Jupyter Notebook] | [http://github.com/jontupitza Jon Tupitza] | ||
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** [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] | ||
| − | + | ** [[Local Linear Embedding (LLE)]] | |
| − | + | ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] ...similar objects are modeled by nearby points | |
| + | ** [http://arxiv.org/pdf/1802.03426.pdf Uniform Manifold Approximation and Projection (UMAP) | L. McInnes, J. Healy, and J. Melville] ... a dimension reduction technique that can be used for visualisation similarly to [[T-Distributed Stochastic Neighbor Embedding (t-SNE) | t-SNE]], but also for general non-linear dimension reduction. | ||
| + | *** [http://github.com/lmcinnes/umap UMAP]...[[Python]] version | ||
| + | *** [http://github.com/pair-code/umap-js UMAP-JS] ...[[Javascript]] version | ||
Related: | Related: | ||
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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. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium] | 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. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium] | ||
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<youtube>YPJQydzTLwQ</youtube> | <youtube>YPJQydzTLwQ</youtube> | ||
Revision as of 07:57, 21 August 2020
Youtube search... ...Google search
- Pooling / Sub-sampling: Max, Mean
- Kernel Trick
- Isomap
- Softmax
- Dimensionality Reduction Techniques Jupyter Notebook | Jon Tupitza
- Embedding functions
To identify the most important Features to address:
- reduce the amount of computing resources required
- 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
- Local Linear Embedding (LLE)
- T-Distributed Stochastic Neighbor Embedding (t-SNE) ...similar objects are modeled by nearby points
- Uniform Manifold Approximation and Projection (UMAP) | L. McInnes, J. Healy, and J. Melville ... a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.
- UMAP...Python version
- UMAP-JS ...Javascript version
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
