Difference between revisions of "Principal Component Analysis"

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| vimeoID1 = 29191853
| vimeoID2 =
| vimeoID3 =
| course_notes_PDF =
| course_notes_alt = Course notes
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| video_download_link_MP4 = http://connectmv.com/media/latent/video/Class-2A.mp4
| video_download_link_MP4_size = 290.1Mb
| video_download_link2_MP4 = http://connectmv.com/media/latent/video/Class-2B.mp4
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| video_download_link3_MP4 = http://connectmv.com/media/latent/video/Class-2C.mp4
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| video_notes1 =
* 00:00 to 21:37 Recap and overview of this class
* 21:38 to 42:01 Preprocessing: centering and scaling
* 42:02 to 57:07 Geometric view of PCA
| video_notes2 =
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{{!}} 00:00 {{!}}{{!}}to{{!}}{{!}} 46:50 {{!}}{{!}} {{!}}{{!}} Details coming soon
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{{!}} 00:00 {{!}}{{!}}to{{!}}{{!}} 1:03:50 {{!}}{{!}} {{!}}{{!}} Details coming soon
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== Class notes ==
== Class notes ==



Revision as of 13:31, 18 September 2011

Video material
Download video: Link (plays in Google Chrome) [290.1Mb]

Video timing

  • 00:00 to 21:37 Recap and overview of this class
  • 21:38 to 42:01 Preprocessing: centering and scaling
  • 42:02 to 57:07 Geometric view of PCA

Class notes

<pdfreflow> class_date = 16 September 2011 [1.65 Mb] button_label = Create my projector slides! show_page_layout = 1 show_frame_option = 1 pdf_file = lvm-class-2.pdf </pdfreflow>

Class preparation

Class 2 (16 September)

  • Reading for class 2
  • Linear algebra topics you should be familiar with before class 2:
    • matrix multiplication
    • that matrix multiplication of a vector by a matrix is a transformation from one coordinate system to another (we will review this in class)
    • linear combinations (read the first section of that website: we will review this in class)
    • the dot product of 2 vectors, and that they are related by the cosine of the angle between them (see the geometric interpretation section)

Class 3 (23 September)

  • Least squares:
    • what is the objective function of least squares
    • how to calculate the two regression coefficients \(b_0\) and \(b_1\) for \(y = b_0 + b_1x + e\)
    • understand that the residuals in least squares are orthogonal to \(x\)
  • Some optimization theory:
    • how an optimization problem is written with equality constraints
    • the Lagrange multiplier principle for solving simple, equality constrained optimization problems