Updated references for doc

Author: terrytangyuan

doc/source/algorithms/itml.rst

@@ -16,4 +16,4 @@ After loading the iris data, we apply ITML:
 
 References
 ------------------
-`Information-theoretic Metric Learning <http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_DavisKJSD07.pdf>`_ Davis, Jason V., et al.
+`Information-theoretic Metric Learning <http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_DavisKJSD07.pdf>`_ Jason V. Davis, et al.

doc/source/algorithms/lfda.rst

@@ -1,8 +1,12 @@
 Local Fisher Discriminant Analysis (LFDA)
 =====================================
+LFDA is a linear supervised dimensionality reduction method and is particularly useful when dealing with multimodality where some class consists of separate clusters. LFDA has an analytic form of the embedding matrix and the solution can be easily computed just by solving a generalized eigenvalue problem. It is thus scalable to large datasets and computationally reliable.
 
 Example Code
 ------------------
 
 References
-------------------
+------------------
+`Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis <http://www.ms.k.u-tokyo.ac.jp/2007/LFDA.pdf>`_ Masashi Sugiyama.
+
+`Local Fisher Discriminant Analysis on Beer Style Clustering <https://gastrograph.com/resources/whitepapers/local-fisher-discriminant-analysis-on-beer-style-clustering.html#>`_ Yuan Tang.

doc/source/algorithms/rca.rst

@@ -7,4 +7,4 @@ Example Code
 
 References
 ------------------
-`Adjustment learning and relevant component analysis <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.2871&rep=rep1&type=pdf>`_ Shental, Noam, et al.
+`Adjustment learning and relevant component analysis <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.2871&rep=rep1&type=pdf>`_ Noam Shental, et al.