Larger residuals, less work: active document scheduling for latent dirichlet allocation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Active spectral clustering via iterative uncertainty reduction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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The technique of spectral clustering is widely used to segment a range of data from graphs to images. Our work marks a natural progression of spectral clustering from the original passive unsupervised formulation to our active semi-supervised formulation. We follow the widely used area of constrained clustering and allow supervision in the form of pair wise relations between two nodes: Must-Link and Cannot-Link. Unlike most previous constrained clustering work, our constraints are specified incrementally by querying an oracle (domain expert). Since in practice, each query comes with a cost, our goal is to maximally improve the result with as few queries as possible. The advantages of our approach include: 1) it is principled by querying the constraints which maximally reduce the expected error, 2) it can incorporate both hard and soft constraints which are prevalent in practice. We empirically show that our method significantly outperforms the baseline approach, namely constrained spectral clustering with randomly selected constraints, on UCI benchmark data sets.