Difference between revisions of "Dimensional Reduction"
| Line 11: | Line 11: | ||
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[Pooling / Sub-sampling: Max, Mean]] | * [[Pooling / Sub-sampling: Max, Mean]] | ||
| − | * [[Kernel | + | * [[Kernel Trick]] |
* [[Isomap]] | * [[Isomap]] | ||
* [[Local Linear Embedding (LLE)]] | * [[Local Linear Embedding (LLE)]] | ||
Revision as of 11:39, 22 June 2019
Youtube search... ...Google search
Algorithms:
- Principal Component Analysis (PCA)
- Pooling / Sub-sampling: Max, Mean
- Kernel Trick
- Isomap
- Local Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Softmax
- Canonical Correlation Analysis (CCA)
- [Independent Component Analysis (ICA)
- 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
- Dimensionality Reduction Techniques Jupyter Notebook | Jon Tupitza
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
