On 12 March 2012 19:30, Andreas <[email protected]> wrote:
> **
> Hi Robert.
> To me, this sounds somwhat like Linear Discriminant Analysis or rather
> Quadratic Discriminant Analysis (without the shrinking part) to me.
>
> In these methods, a Gaussian is fitted to each class and classification
> is done by finding the Gaussian that most likely created a data point.
>
> This is basically the same as finding the mean of each class and
> classifying to the nearest using Mahalanobis distance.
>
> I didn't look at the paper but that sounded quite related.
>
> There is no probabilistic way to get the feature-selection shrinking in
> this framework,
> I guess, but of course you can always just set entries of the mean to zero.
>
>
> Maybe you can take a closer look at these methods and work out
> what the differences are.
>
> Hope that helps,
> Andy
>
>
>
> On 03/12/2012 04:35 AM, Robert Layton wrote:
>
> Hi All,
>
> On reading some research, it appears that the shrunken centroid
> classifier<http://www-stat.stanford.edu/%7Etibs/PAM/Rdist/howwork.html>is one
> of the better methods for authorship analysis.
> Therefore, I'm going to implement it at see if it really is, and I was
> planning to add it to scikits.learn.
>
> Before I start, I wanted to make sure it wasn't already in scikits.learn
> under a different name (as I don't do much classification, I am not sure).
> The method is basically like k-means clustering:
> training: each class is represented by its centroid
> testing: instances are assigned to the nearest centroid.
>
> That is nearest centroid classification, while the "shrunken" bit
> basically a feature selection.
> Each centroid is moved towards the dataset centroid (set to 0) by a
> threshold value. If any feature crosses over zero, it is set to zero,
> effectively eliminating some features from the classification.
>
> In my short research on the subject, I've seen two types of threshold. The
> first is the absolute amount to move the point towards the dataset centroid
> (i.e. 2.0 units), while the second is the number of features to reduce each
> centroid to.
>
> My question is: does scikits.learn have anything already? If not, I'll
> start working on it soon.
>
> Thanks,
>
> Robert
>
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Hi Andy,
That sounds pretty correct. My guess is that they are different but highly
related, as you said.
I'll do some investigation.
Thanks,
Robert
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