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Non-Convex Robust PCA with Special Norm

Non-Convex Robust PCA with Special Norm
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2020-02-03 12:49
FunctionCoordinate GeometryOptimization
This example visualizes how a special norm works in non-convex robust PCA. It helps understand how special norms affect the results of PCA in non-convex optimization problems. Key mathematical concepts include non-convex optimization and robust PCA. GeoGebra commands used include Min, Midpoint, Distance, OrthogonalLine, Intersect, and Segment.

GeoGebra Commands & Parameters

  1. Min(h, 0, (sigma * 5)) → A
  2. Midpoint(A, ClosestPoint[xAxis, A]) →
  3. Distance(A, xAxis) → distanceAxAxis
  4. Midpoint(A, ClosestPoint[xAxis, A]) →
  5. Distance(A, yAxis) → distanceAyAxis
  6. Midpoint(A, ClosestPoint[yAxis, A]) →
  7. OrthogonalLine(A, xAxis) → i
  8. OrthogonalLine(A, yAxis) → j
  9. Intersect(j, yAxis) → B
  10. Segment(A, B) → k
  11. Intersect(i, xAxis) → C
  12. Segment(A, C) → l

AI Drawing Prompt Reference

Draw the impact of a special norm in non-convex optimization. First, find the shortest distances from point A to the x-axis and y-axis, then construct the perpendicular lines from A to these axes, and finally connect point A to the intersection points B and C on the axes to form segments AB and AC.
The prompt above was compiled from the original drawing commands and is for reference only.