<p>The most common thing to do is train <a href="http://en.wikipedia.org/wiki/Autoencoder" rel="noreferrer">an autoencoder</a>, where the desired outputs are equal to the inputs. This makes the network try to learn a representation that best "compresses" the input distribution.</p>

<p><a href="http://www.freepatentsonline.com/5590218.html" rel="noreferrer">Here's a patent</a> describing a different approach, where the output labels are assigned randomly and then sometimes flipped based on convergence rates. It seems weird to me, but okay.</p>

<p>I'm not familiar with other methods that use backpropogation for clustering or other unsupervised tasks. Clustering approaches with ANNs seem to use other algorithms (<a href="http://arxiv.org/pdf/cs/0608115.pdf" rel="noreferrer">example 1</a>, <a href="http://www.rimtengg.com/coit2007/proceedings/pdfs/40.pdf" rel="noreferrer">example 2</a>).</p>
Machine Learning: Unsupervised Backpropagation
<p>I'm having trouble with some of the concepts in machine learning through neural networks. One of them is <a href="http://en.wikipedia.org/wiki/Delta_Rule" rel="noreferrer">backpropagation</a>. In the weight updating equation, </p>

<pre><code>delta_w = a*(t - y)*g'(h)*x
</code></pre>

<p><code>t</code> is the "target output", which would be your class label, or something, in the case of supervised learning. But what would the "target output" be for unsupervised learning?</p>

<p>Can someone kindly provide an example of how you'd use BP in unsupervised learning, specifically for clustering of classification?</p>

<p>Thanks in advance.</p>