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lbugnon avatar lbugnon commented on May 28, 2024

(Edited)
Mark as resolved, this is already working with param n_components

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drcege avatar drcege commented on May 28, 2024

The package in pypi is not updated?
Still AssertionError: n_components should be 2

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smt-HS avatar smt-HS commented on May 28, 2024

I encountered the same issue

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DmitryUlyanov avatar DmitryUlyanov commented on May 28, 2024

Updated the package. Please try now.

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aCampello avatar aCampello commented on May 28, 2024

Adding to the discussion, although this is possible in practice, TSNE might not be the method you are looking for to reduce to higher dimensions. A word of caution was provided by the T-SNE authors themselves in the original paper VISUALIZING DATA USING T-SNE:

It is not obvious how t-SNE will perform on the more general task of dimensionality reduction (i.e., when the dimensionality of the data is not reduced to two or three, but to d > 3 dimensions). To simplify evaluation issues, this paper only considers the use of t-SNE for data visualization. The behavior of t-SNE when reducing data to two or three dimensions cannot readily be extrapolated to d > 3 dimensions because of the heavy tails of the Student-t distribution. In high-dimensional spaces, the heavy tails comprise a relatively large portion of the probability mass under the Student-t distribution, which might lead to d-dimensional data representations that do not preserve the local structure of the data as well. Hence, for tasks in which the dimensionality of the data needs to be reduced to a dimensionality higher than three, Student t-distributions with more than one degree of freedom10 are likely to be more appropriate.

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