<p>I suppose that by LDA you mean Latent Dirichlet Allocation, if you mean Linear Discriminant Analysis you can simply use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" rel="nofollow">sklearn.discriminant_analysis.LinearDiscriminantAnalysis</a> as a classifier and compare accuracy or precision and recall or whatever.</p>

<p>You should use Latent Dirichlet Allocation as a transformer before feeding the topics representation to SVM. For instance the following code does just that (you could of course use Pipelines and cross validation but that is just an example).</p>

<pre><code>import numpy as np
from sklearn.datasets import fetch_olivetti_faces
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.linear_svm import LinearSVC

# get the dataset
faces = fetch_olivetti_faces()
X = (faces.data*255).astype(int)
y = faces.target

# create a test set and a training set
idx = np.arange(len(X))
np.random.shuffle(idx)
train = idx[:2*len(X)/3]
test = idx[2*len(X)/3:]

# create the models
lda = LatentDirichletAllocation(n_topics=10)
svm = LinearSVC(C=10)

# evaluate everything
lda.fit(X[train])
T = lda.transform(X)
print svm.fit(T[train], y[train]).score(T[test], y[test])
</code></pre>

<p>LDA is not particularly <a href="https://arxiv.org/abs/1003.0783" rel="nofollow">well suited for classification</a> and thus there have been developed quite a few variations. We developed one such supervised variation for classification which we presented at ACM Multimedia this year. You can read our paper <a href="http://mug.ee.auth.gr/wp-content/uploads/fsLDA.pdf" rel="nofollow">Fast Supervised LDA</a> and get code and documentation from <a href="http://ldaplusplus.com/" rel="nofollow">http://ldapluplus.com/</a>. Finally you can see an <a href="http://ldaplusplus.com/topic-inference-visualization/" rel="nofollow">example of using Olivetti Faces with LDA++</a>.</p>
How can I use skylearn module in python for face recognition using AT & T database(fetch_olivetti_faces data sets.)
<p>I want to benmark the performance of face recognition using SVM and LDA. Could you please give me an idea of how to implement it</p>