Skip to content
GitLab
  • Menu
Projects Groups Snippets
  • /
  • Help
    • Help
    • Support
    • Community forum
    • Submit feedback
  • Sign in
  • Data Driven Engineering Data Driven Engineering
  • Project information
    • Project information
    • Activity
    • Members
  • Repository
    • Repository
    • Files
    • Commits
    • Branches
    • Tags
    • Contributors
    • Graph
    • Compare
    • Locked Files
  • Deployments
    • Deployments
    • Releases
  • Packages & Registries
    • Packages & Registries
    • Package Registry
    • Infrastructure Registry
  • Wiki
    • Wiki
  • Snippets
    • Snippets
  • Activity
  • Graph
  • Commits
Collapse sidebar
  • cihan.ates
  • Data Driven EngineeringData Driven Engineering
  • Wiki
  • Dde 1
  • Dimensionality reduction

Dimensionality reduction · Changes

Page history
Update Dimensionality reduction authored Dec 01, 2021 by cihan.ates's avatar cihan.ates
Hide whitespace changes
Inline Side-by-side
DDE-1/Dimensionality-reduction.md
View page @ a877a5af
......@@ -69,7 +69,7 @@ S = X^T X
S = 1/N \sum_{n=1}^{N} (x_n - \overline{x})(x_n-\overline{x})^T
```
In the third step, we maximize the variance of the projected data onto new coordinate system. This step consists of several smaller steps.
In the third step, we maximize the variance of the data projected onto the new coordinate system. This step consists of several smaller steps.
Let's think about what we just said. We are after a new coordinate system and we will project our data onto this new coordinates (think about the conversion from the Cartesian to spherical coordinates). In this new space, we will again have its own unit vectors indicating the directions of the coordinates (like x,y,z => r, θ, Φ). Let call the direction vector as $`u_{1}`$ . Note that this vector will have M dimension, like our data set.
......
Clone repository
Home
  • Recommended resources
  • Toolbox
  • Miscellaneous topics
DDE 1: ML for Dynamical systems
  • Complex Systems
  • Ode to Learning
  • Statistics
  • Regression
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Outlier Detection
DDE 2: Advanced topics
  • Evolutionary learning

Imprint