Light LDA doc (dotnet#464)

Author: ganik

src/Microsoft.ML.Transforms/Text/LdaTransform.cs

@@ -27,8 +27,21 @@
 namespace Microsoft.ML.Runtime.TextAnalytics
 {
     /// <summary>
-    /// The latent Dirichlet allocation (LDA) transform. 
-    /// http://arxiv.org/abs/1412.1576 
+    /// LightLDA transform: Big Topic Models on Modest Compute Clusters.
+    /// <see href="http://arxiv.org/abs/1412.1576">LightLDA</see> is an implementation of Latent Dirichlet Allocation (LDA).
+    /// Previous implementations of LDA such as SparseLDA or AliasLDA allow to achieve massive data and model scales,
+    /// for example models with tens of billions of parameters to be inferred from billions of documents.
+    /// However this requires using a cluster of thousands of machines with all ensuing costs to setup and maintain.
+    /// LightLDA solves this problem in a more cost-effective manner by providing an implementation 
+    /// that is efficient enough for modest clusters with at most tens of machines... 
+    /// For more details please see original LightLDA paper: 
+    /// http://arxiv.org/abs/1412.1576
+    /// http://www.www2015.it/documents/proceedings/proceedings/p1351.pdf
+    /// and open source implementation: 
+    /// https://github.com/Microsoft/LightLDA
+    /// 
+    /// See <a href="https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs"/>
+    /// for an example on how to use LdaTransform.
     /// </summary>
     public sealed class LdaTransform : OneToOneTransformBase
     {