I got 168, because I use log base 2 instead of e.
([?]) if memory serves right, I read it in entropy definition that people
normally use base 2, so I just assumed it was 2 in code. (my bad).
And now I have a better understanding, so thank you both for the
explanation.
On Fri, Apr 12, 2013 at
Yes that's true, it is more usually bits. Here it's natural log / nats.
Since it's unnormalized anyway another constant factor doesn't hurt and it
means not having to change the base.
On Fri, Apr 12, 2013 at 8:01 AM, Phoenix Bai baizh...@gmail.com wrote:
I got 168, because I use log base 2
I am having trouble understanding whether the following code is sufficient
for running PCA
I have a sequence file of dense vectors that I am calling and then I am
trying to run the following code
SSVDSolver pcaFactory = new SSVDSolver(conf, new Path(vectorsFolder), new
That looks like the best shortcut. It is one of the few places where the rows
of one and the columns of the other are seen together. Now I know why you
transpose the first input :-)
But, I have begun to wonder whether it is the right thing to do for a cross
recommender because you are
No,this is not right.
I will explain later when i have a moment.
On Apr 12, 2013 8:08 AM, Chirag Lakhani clakh...@zaloni.com wrote:
I am having trouble understanding whether the following code is sufficient
for running PCA
I have a sequence file of dense vectors that I am calling and then I
On Fri, Apr 12, 2013 at 8:42 AM, Dmitriy Lyubimov dlie...@gmail.com wrote:
No,this is not right.
I will explain later when i have a moment.
On Apr 12, 2013 8:08 AM, Chirag Lakhani clakh...@zaloni.com wrote:
I am having trouble understanding whether the following code is sufficient
for
The only virtue of using the natural base is that you get a nice asymptotic
distribution for random data.
On Fri, Apr 12, 2013 at 1:10 AM, Sean Owen sro...@gmail.com wrote:
Yes that's true, it is more usually bits. Here it's natural log / nats.
Since it's unnormalized anyway another
Log-likelihood similarity is a bit of a force-fit of the concept of the
LLR. It is basically a binarizing and sparsifying filter applied to
cooccurrence counts.
As such, it is eminently suited to implementation using a matrix multiply.
On Fri, Apr 12, 2013 at 8:35 AM, Pat Ferrel
Hi all,
We're (ab)using LibLinear (linear SVM) as a multi-class classifier, with 200+
labels and 400K features.
This results in a model that's 800MB, which is a bit unwieldy. Unfortunately
LibLinear uses a full array of weights (nothing sparse), being a port from the
C version.
I could do
