Sorry, my fault.
Supervised PCA is different to Linear Discriminant Analysis. It uses a
heuristic to keep only the variables that show some correlation with the
response when calculating the components. It does not incorporate
explicitly the class separation as an objective. Supervised PCA can be
He was asking about Linear Discriminant Analysis, not Latent Dirichlet
Allocation.
Mathieu
On Thu, Jul 30, 2015 at 7:58 PM, Stylianos Kampakis <
stylianos.kampa...@gmail.com> wrote:
> Hi Sebastian,
>
> LDA is unsupervised. Supervised PCA finds components correlated with the
> response variable.
Hi Sebastian,
LDA is unsupervised. Supervised PCA finds components correlated with the
response variable.
Best regards,
Stelios
2015-07-29 22:55 GMT+01:00 Sebastian Raschka :
> Out of curiosity, how does supervised PCA compare to LDA (Linear
> Discriminant Analysis); in a nutshell, what would b
My feeling is that it will perform better in cases where there are clusters
of correlated attributes, which the exact same case where it would make
sense to use a dimensionality reduction technique such as factor analysis
or PCA.
Hastie et al. in their book "Elements of Statistical Learning" propo
Out of curiosity, how does supervised PCA compare to LDA (Linear Discriminant
Analysis); in a nutshell, what would be the main difference?
Best,
Sebastian
> On Jul 29, 2015, at 5:41 PM, Stylianos Kampakis
> wrote:
>
> Hi Andreas,
>
> Sure. Actually, the purpose of the model is both regulariz
Indeed it sounds interesting but I'd still be curious as to how it
compares against elasticnet.
On 07/29/2015 05:41 PM, Stylianos Kampakis wrote:
Hi Andreas,
Sure. Actually, the purpose of the model is both regularization and
dimensionality reduction for problems where the number of features
Hi Andreas,
Sure. Actually, the purpose of the model is both regularization and
dimensionality reduction for problems where the number of features can be
larger than the number of instances (or in any case when there is a large
number of features). It is particularly effective when there are lots
Hi Stylianos.
Can you give a bit more background on the model?
It seems fairly well-cited but I haven't really seen it in practice.
Is it still state of the art?
The main purpose seems to be a particular type of regularization, right,
not supervised dimensionality reduction?
How does this compar
Dear all,
I am thinking to contribute a new model to the library: The supervised
principal components analysis by Bair et al. (2006).
I wanted to get in touch before contributing to make sure no-one else is
working on that algorithm, since this is what the site recommends.
Cheers,
S. Kampakis
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