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View Code? Open in Web Editor NEWA tutorial on the t-SNE learning algorithm
License: Other
A tutorial on the t-SNE learning algorithm
License: Other
Thanks for informative and concise tutorial,
I am wondering if the definition of the similarity of the map points should be based on Y points and not X points.
it is
(q_{ij} = \frac{f(\left| x_i - x_j\right|)}{\displaystyle\sum_{k \neq i} f(\left| x_i - x_k\right|)}
and should be
(q_{ij} = \frac{f(\left| y_i - y_j\right|)}{\displaystyle\sum_{k \neq i} f(\left| y_i - y_k\right|)}
Is it correct?
Is there currently or will there be in the near future a conda install option?
is it possible to visualize UCF 101 or UCF crime datasetset using t-SNE ?
how we can use tsne for a semantic segmentation to have such visualization?
Part of the code:
X = np.vstack(u[:,i] for i in range(n_classes))
Y = np.hstack(i for i in range(n_classes))
final = tsne.fit_transform(X)
Question :
The output visualization has only seven points, not seven clusters.
How can I solve this problem? Thank you.
not sure what python library would you recommend for doing this?
there is a matlab tamplate for that, but i just a bit hesitate to switch http://cs.stanford.edu/people/karpathy/cnnembed/
thanks
HSI images are basically maps where each pixel has a different class.
Suppose there is a 145*145 image with 200 channels. The following map image will have 16 unique class labels assigned to individual pixels. how does this get visualized in tsne?
Hello Team,
versions post sklearn 0.15.2 seem to have different package structure which renders monkey patching as proposed currently ineffective.
Suggested updates:
# Import for _gradient_descent function to be monkey patched
from time import time
and
sklearn.manifold._t_sne._gradient_descent = _gradient_descent
it should be as follow:
def _joint_probabilities_constant_sigma(D, sigma):
P = np.exp(-D**2/(2 * sigma**2))
P /= np.sum(P, axis=1)
return P
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