Difference between revisions of "Dimensional Reduction"
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[http://www.youtube.com/results?search_query=Dimensional+Reduction+Algorithm Youtube search...] | [http://www.youtube.com/results?search_query=Dimensional+Reduction+Algorithm Youtube search...] | ||
[http://www.google.com/search?q=Dimensional+Reduction+Algorithm+Dimension+machine+learning+ML ...Google search] | [http://www.google.com/search?q=Dimensional+Reduction+Algorithm+Dimension+machine+learning+ML ...Google search] | ||
Revision as of 23:55, 2 February 2019
Youtube search... ...Google search
- Principle Component Analysis (PCA)
- Kernel Approximation
- Isomap
- Local Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Softmax
- Pooling / Sub-sampling: Max, Mean
- (Deep) Convolutional Neural Network (DCNN/CNN)
- 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
