DOC add link plot_tomography_l1_reconstruction (scikit-learn#30070)

Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com>

Author: claudio1975

doc/modules/feature_selection.rst

@@ -224,6 +224,9 @@ alpha parameter, the fewer features selected.
   noise, the smallest absolute value of non-zero coefficients, and the
   structure of the design matrix X. In addition, the design matrix must
   display certain specific properties, such as not being too correlated.
+  On the use of Lasso for sparse signal recovery, see this example on 
+  compressive sensing: 
+  :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`.
 
   There is no general rule to select an alpha parameter for recovery of
   non-zero coefficients. It can by set by cross-validation