Updated readme

README.md

@@ -1,20 +1,36 @@
-IPCA
+IPCA
 ====
 
-This package implements Ideal PCA in MATLAB.
+This package implements Ideal PCA in MATLAB. 
 
-Usage
------
+Ideal PCA is a cross-kernel based feature extraction algorithm which is (a) a faster alternative to kernel PCA and (b) a method to learn data manifold certifying features.
 
+For a derivation of the algorithm and details, see
 
-See the inline documentation in IPCA.m and IPCA_eval.m.
+Franz J. Király, Martin Kreuzer, Louis Theran. *Learning with Cross-Kernels and IPCA*. http://arxiv.org/abs/1406.2646
 
-References
-----------
 
-Franz J. Király, Martin Kreuzer, Louis Theran. *Learning with Cross-Kernels and IPCA*. http://arxiv.org/abs/1406.2646
+Getting started
+---------------
+
+Copy all files to a directory in your MATLAB path.
+
+The release contains a template script example_script.m which needs to be completed before running. See the file for further explanation.
+
+The IPCA algorithm is encapsulated in two routines:
+
+IPCA pre-computes the cross-kernel matrices and other parameters;
+
+IPCA_eval takes the output of IPCA and converts it into a collection of output features which can be specified by the user, depending on the learning task.
+
+See the inline documentation in IPCA.m and IPCA_eval.m for usage syntax.
+
 
 License
 -------
 
-See the file LICENSE.
+Copyright (c) 2014, Franz J. Király, Martin Kreuzer, Louis Theran.
+
+This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3, as published by the Free Software Foundation. See the file LICENSE.
+
+This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

example_script.m

@@ -3,7 +3,7 @@
 % needs to be completed before use
 
 
-%STEP 1 - load data.
+% STEP 1 - load data.
 
 data_to_learn_features = %read in data from which features are learnt
 data_to_evaluate_features = %read in data for which to extract features, these canbut need not be the same