diff --git a/Rakefile b/Rakefile
index cf700dd..fb35c96 100644
--- a/Rakefile
+++ b/Rakefile
@@ -43,7 +43,7 @@ task :test => :check_dependencies
 
 task :default => :test
 
-require 'rake/rdoctask'
+require 'rdoc/task'
 Rake::RDocTask.new do |rdoc|
   version = File.exist?('VERSION') ? File.read('VERSION') : ""
 
diff --git a/VERSION b/VERSION
index 167b000..389facc 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.5.4
\ No newline at end of file
+0.5.5
\ No newline at end of file
diff --git a/lib/svd_matrix.rb b/lib/svd_matrix.rb
index 2e38d22..ac37642 100644
--- a/lib/svd_matrix.rb
+++ b/lib/svd_matrix.rb
@@ -3,17 +3,13 @@
 
 class SVDMatrix < Matrix
   public_class_method :new
-  
+  public :"[]=", :set_element, :set_component
   # Create a new SVD Matrix with m rows, n columns
   def initialize(m, n)
+    @row_size = m
     @rows = Array.new(m)
     @column_size = n
-    m.times {|i| @rows[i] = Array.new(n)}
-  end
-  
-  # Set the value of the cell i, j
-  def []=(i, j, val)
-    @rows[i][j] = val
+    m.times {|i| @rows[i] = Array.new(n) {0} }
   end
   
   # Set the value of a row to an array
@@ -37,23 +33,37 @@ def inspect
   # [ 0, 0, 0, 0 ]
   def decompose(reduce_dimensions_to = nil)
     input_array = []
-    @rows.each {|row| input_array += row}
-    u_array, w_array, v_array = SVD.decompose(input_array, row_size, column_size)
-    
-    # recompose U matrix
-    u = SVDMatrix.new(row_size, reduce_dimensions_to || column_size)
-    row_size.times {|i| u.set_row(i, u_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
-    
-    # recompose V matrix
-    v = SVDMatrix.new(column_size, reduce_dimensions_to || column_size)
-    column_size.times {|i| v.set_row(i, v_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
-    
-    # diagonalise W array as a matrix
-    if reduce_dimensions_to
-      w_array = w_array[0...reduce_dimensions_to]
-    end
-    w = Matrix.diagonal(*w_array)
-    
+
+    if @row_size >= @column_size
+
+      @rows.each {|row| input_array += row}
+      u_array, w_array, v_array = SVD.decompose(input_array, row_size, column_size)
+      
+      # recompose U matrix
+      u = SVDMatrix.new(row_size, reduce_dimensions_to || column_size)
+      row_size.times {|i| u.set_row(i, u_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
+      
+      # recompose V matrix
+      v = SVDMatrix.new(column_size, reduce_dimensions_to || column_size)
+      column_size.times {|i| v.set_row(i, v_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
+      
+      # diagonalise W array as a matrix
+      if reduce_dimensions_to
+        w_array = w_array[0...reduce_dimensions_to]
+      end
+      w = Matrix.diagonal(*w_array)
+
+    else
+
+      transpose = SVDMatrix.new( @column_size, @row_size )
+      @rows = @rows.transpose
+      for i in 0..@column_size-1 
+        transpose.set_row(i, @rows[i].to_a )
+      end
+      v , w, u = transpose.decompose( reduce_dimensions_to || @row_size )
+
+    end  
+
     [u, w, v]
   end