  Large databases are often organized by hand-labeled metadata, or criteria,
which are expensive to collect. We can use unsupervised learning to model
database variation, but these models are often high dimensional, complex to
parameterize, or require expert knowledge. We learn low-dimensional continuous
criteria via interactive ranking, so that the novice user need only describe
the relative ordering of examples. This is formed as semi-supervised label
propagation in which we maximize the information gained from a limited number
of examples. Further, we actively suggest data points to the user to rank in a
more informative way than existing work. Our efficient approach allows users to
interactively organize thousands of data points along 1D and 2D continuous
sliders. We experiment with datasets of imagery and geometry to demonstrate
that our tool is useful for quickly assessing and organizing the content of
large databases.
