DOC use notebook-style for plot_faces_decomposition
@@ -529,7 +529,7 @@ dictionary fixed, and then updating the dictionary to best fit the sparse code.
:target: ../auto_examples/decomposition/plot_faces_decomposition.html
:scale: 60%
-.. |dict_img2| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_006.png
+.. |dict_img2| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_007.png
:target: ../auto_examples/decomposition/plot_faces_decomposition.html
:scale: 60%
@@ -548,19 +548,19 @@ different positivity constraints applied. Red indicates negative values, blue
indicates positive values, and white represents zeros.
-.. |dict_img_pos1| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_011.png
+.. |dict_img_pos1| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_010.png
:target: ../auto_examples/decomposition/plot_image_denoising.html
:scale: 60%
-.. |dict_img_pos2| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_012.png
+.. |dict_img_pos2| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_011.png
:target: ../auto_examples/decomposition/plot_image_denoising.html
:scale: 60%
-.. |dict_img_pos3| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_013.png
+.. |dict_img_pos3| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_012.png
:target: ../auto_examples/decomposition/plot_image_denoising.html
:scale: 60%
-.. |dict_img_pos4| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_014.png
+.. |dict_img_pos4| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_013.png
:target: ../auto_examples/decomposition/plot_image_denoising.html
:scale: 60%
@@ -689,7 +689,7 @@ about these components (e.g. whether they are orthogonal):
:target: ../auto_examples/decomposition/plot_faces_decomposition.html
:scale: 60%
-.. |fa_img3| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_009.png
+.. |fa_img3| image:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_008.png
:target: ../auto_examples/decomposition/plot_faces_decomposition.html
:scale: 60%
@@ -699,7 +699,7 @@ The main advantage for Factor Analysis over :class:`PCA` is that
it can model the variance in every direction of the input space independently
(heteroscedastic noise):
-.. figure:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_008.png
+.. figure:: ../auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_009.png
:target: ../auto_examples/decomposition/plot_faces_decomposition.html
:align: center
:scale: 75%
