LibreOffice in Fedora 14

Installing LibreOffice in Fedora 14
This is a short note for myself (also for the search engines).
  1. Download LibreOffice tarballs from
  2. Extract the RPMs and install the RPMs as per instructions here
  3. Delete or move in /opt/libreoffice3.4/ure/lib

The RPMs from LibreOffice install to /opt and it has its own copy of Unfortunately, this version is unable to link(?) to the GStreamer / FFMPEG libraries. By removing the file from /opt LibreOffice is forced to use the system Now, LibreOffice can use video files in Impress with no problem.

I don't know what instabilities this may introduce LibreOffice, use at your own risk.


Support House Bills for the Deaf

We would like to get 150,000 signatures within this month to pass two laws:

  • House Bill 4121: Sign language insets for TV news programs
  • House Bill 4631: Court interpreters for the Deaf

Learn more about the House Bills and sign our petition list at


General Assembly for Sign Language Interpreters

To all hearing interpreters , sign language enthusiasts, anybody interested to be sign language interpreters; to all deaf interpreters and deaf individuals:

There will be a general assembly meeting for sign language interpreters

Date: 2011 May 21 (Saturday)
Time: 7:30 am - 3:00 pm
CAP Development center
7th floor, CAP Building
126 Amorsolo corner VA Rufino
Legaspi Village, Makati

If you are from outside Manila and cannot come to Makati, the assembly will be webcast, e-mail to get the instructions. Place in the subject "SLI webcast".

You should have a Yahoo account ( or a Skype account ( to be able to view the webcast.


Manifold Learning, part 3

Part 1 here
Part 2 here

Non-linear dimensionality reduction

In an earlier post, we saw we can approximate higher dimensional data using dimensionality reduction techniques. However, PCA (and similar methods) have a weakness. It requires the constraints to line along or near straight lines. What about data that lie along curves?

Another Illustrated Example

Let us pretend again we have 2-dimensional data and our tools can only analyse one-dimensional data. We cannot use the trick we used earlier because the data no longer lies close to a straight line. However, we can cheat. Looking at it closely, we can see that our data (almost) lies along a spiral.

Just like before, we are going to use the projections of our data (red circles) onto the spiral as approximations of our data. We can do this as long as our data is "very close" to the spiral.

The final trick is to "unroll" the spiral and pretend it is a straight line. We can now use our tools for analysis. Just like in our previous approximation errors are introduced. As long as the data is "very close" to the spiral, the error introduced is small.

You may also notice that I skipped over how we actually unroll the spiral. We'll discuss one way to do it in the next post.