It might improve the quality of the embedding at the expense of speed. For
instance, you might want to try
a density of 1 / 3 which is the value used in the Achlioptas paper [1].
For the number of components, you might want to play with the
johnson_lindenstrauss_min_dim [2]
to get a better idea on the quality of your embeding. The number of components
will greatly depends on you number
of samples and epsilon-quality of the embedding.
Best regards,
Arnaud
[1] Database-friendly random projections: Johnson-Lindenstrauss with binary
coins. Dimitris Achlioptas. Microsoft Research
[2]
http://scikit-learn.org/dev/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html#sklearn.random_projection.johnson_lindenstrauss_min_dim
On 08 Aug 2014, at 13:22, Philipp Singer <kill...@gmail.com> wrote:
> I always normalize X prior to the random projection as I have observed that
> this always produces more accurate results (same for LSA/SVD).
>
> Have not tried to increase eps as this would lead to much less features and
> more error. I am also not sure how I should alter the density parameter. I
> feel safer to use it to the auto value which calculates it according to the
> Li et al paper. Could you recommend some value?
>
> I think I will be more effective with LSA for now. Are there any specific
> recommendations for the number of components? Chose 300 for now.
>
> Best,
> Philipp
>
> Am 08.08.2014 um 13:14 schrieb Arnaud Joly <a.j...@ulg.ac.be>:
>
>> Have you tried to increase the number of components or epsilon parameter and
>> density of the SparseRandomProjection?
>> Have you tried to normalise X prior the random projection?
>>
>> Best regards,
>> Arnaud
>>
>> On 08 Aug 2014, at 12:19, Philipp Singer <kill...@gmail.com> wrote:
>>
>>> Just another remark regarding this:
>>>
>>> I guess I can not circumvent the negative cosine similarity values. Maybe
>>> LSA is a better approach? (TruncatedSVD)
>>>
>>> Am 08.08.2014 um 10:35 schrieb Philipp Singer <kill...@gmail.com>:
>>>
>>>> Hi,
>>>>
>>>> I asked a question about the sparse random projection a few days ago, but
>>>> thought I should start a new topic regarding my current problem.
>>>>
>>>> I am calculating TFIDF weights for my text documents and then calculate
>>>> cosine similarity between documents for determining the similarity between
>>>> documents. For dimensionality reduction I am using the Sparse Random
>>>> Projection class.
>>>>
>>>> My current process looks like the following:
>>>>
>>>> docs = [text1, text2,…]
>>>> vec = TfidfVectorizer(max_df=0.8)
>>>> X = vec.fit_transform(docs)
>>>> proj = SparseRandomProjection()
>>>> X2 = proj.fit_transform(X)
>>>> X2 = normalize(X2) #for L2 normalization
>>>> sim = X2 * X2.T
>>>>
>>>> It works reasonable well. However, I found out that the sparse random
>>>> projection sets many weights to a negative value. Hence, also many
>>>> similarity scores end up being negative. Given the original intention of
>>>> tfidf weights (which should never be negative) and corresponding cosine
>>>> similarity scores (which then should always only range between zero and
>>>> one), I do not know whether this is an appropriate approach for my task.
>>>>
>>>> Hope someone has some advice. Maybe I am also doing something wrong here.
>>>>
>>>> Best,
>>>> Philipp
>>>>
>>>
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