Difference between revisions of "Principal Component Analysis (PCA)"
| Line 14: | Line 14: | ||
* [[Dimensional Reduction Algorithms]] | * [[Dimensional Reduction Algorithms]] | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
| − | * [http://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml Independent Component Analysis (ICA) | University of Helsinki] | + | * [[Causation vs. Correlation]] - Multivariate Additive Noise Model (MANM) |
| + | ** [http://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml Independent Component Analysis (ICA) | University of Helsinki] | ||
** [http://www.cs.helsinki.fi/u/ahyvarin/papers/JMLR06.pdf Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) | S. Shimizu, P. Hoyer, A. Hyvarinen, and A. Kerminen - University of Helsinki] | ** [http://www.cs.helsinki.fi/u/ahyvarin/papers/JMLR06.pdf Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) | S. Shimizu, P. Hoyer, A. Hyvarinen, and A. Kerminen - University of Helsinki] | ||
** [http://archive.org/details/arxiv-1104.2808/page/n15 Greedy DAG Search (GDS) | Alain Hauser and Peter Biihlmann] | ** [http://archive.org/details/arxiv-1104.2808/page/n15 Greedy DAG Search (GDS) | Alain Hauser and Peter Biihlmann] | ||
| + | ** [http://auai.org/uai2017/proceedings/papers/250.pdf Feature-to-Feature Regression for a Two-Step Conditional Independence Test | Q. Zhang, S. Filippi, S. Flaxman, and D. Sejdinovic] | ||
a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis. | a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis. | ||
Revision as of 11:03, 22 June 2019
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Clustering
- Anomaly Detection
- Dimensional Reduction Algorithms
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Causation vs. Correlation - Multivariate Additive Noise Model (MANM)
- Independent Component Analysis (ICA) | University of Helsinki
- Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) | S. Shimizu, P. Hoyer, A. Hyvarinen, and A. Kerminen - University of Helsinki
- Greedy DAG Search (GDS) | Alain Hauser and Peter Biihlmann
- Feature-to-Feature Regression for a Two-Step Conditional Independence Test | Q. Zhang, S. Filippi, S. Flaxman, and D. Sejdinovic
a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis.
- Center (and standardize) data
- First principal component axis
- Across centroid of data cloud
- Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud
- Second principal component axis
- Orthogonal to first principal component
- Along maximum variation in the data
- First PCA axis becomes x-axis and second PCA axis y-axis
- Continue process until the necessary number of principal components is obtained
NumXL
