Motivation I : Data Compression
 Useful for reduce the computation and less data input making faster process for learning algorithm
 We may not be the one that handle all the features. Often another member in team give additional features. Maybe many member gather theirs to one big chunk of features
 This make it highly redundant features that often risk being the same features
 The example talks about cm and inch, which is the same parameter, length.
 Or maybe the example of the pilot in helicopter
 Example the correlaction between engagment and skill is aptitude
 If we try to visualize the graph between 2 similar parameters, we can see the linear projection betweent the 2 input(Graph shown above, linear increase).
 What we need is the line that visualize the projection and makes that as 1D input
 output is the position of each point in projection (green line)
 Later discussed being better than the memory reduce or data reduce
 Reduce 3D to 2D
 Often faced about 1000D, try to reduce to 100D
 Ng said that thousandD impossible to simulate in slide, so he is doing 3D
 The example show about 3D structure. Again, if we observe closely, the 3D makes some linearity increase in graph.
 If we can somehow make a 2Dprojection (plane) that represent the 3Dstructure
 try to project 3D to a plane, and record the 3D position (x,y,z) of a plane. Then we can plot the data based on the 2D graph
 Convert 2D in matrix as z1 and z2

Include 3 process data structure
 3D data Structure
 2D Plane in 3D data structure
 2D graph
 Later, make the resulting dimensional resulting to faster process of learning algorithm