389 research outputs found
Constrained Clustering: Effective Constraint Propagation with Imperfect Oracles
Abstract—While spectral clustering is usually an unsuper-vised operation, there are circumstances in which we have prior belief that pairs of samples should (or should not) be assigned with the same cluster. Constrained spectral clustering aims to exploit this prior belief as constraint (or weak supervision) to influence the cluster formation so as to obtain a structure more closely resembling human perception. Two important issues re-main open: (1) how to propagate sparse constraints effectively, (2) how to handle ill-conditioned/noisy constraints generated by imperfect oracles. In this paper we present a unified framework to address the above issues. Specifically, in contrast to existing constrained spectral clustering approaches that blindly rely on all features for constructing the spectral, our approach searches for neighbours driven by discriminative feature selection for more effective constraint diffusion. Crucially, we formulate a novel data-driven filtering approach to handle the noisy constraint problem, which has been unrealistically ignored in constrained spectral clustering literature. Keywords-Constrained clustering, constraint propagation, feature selection, imperfect oracles, spectral clustering. I
Performance Following: Real-Time Prediction of Musical Sequences Without a Score
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Video semantic clustering with sparse and incomplete tags
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Clustering tagged videos into semantic groups is important but challenging due to the need for jointly learning correlations between heterogeneous visual and tag data. The task is made more difficult by inherently sparse and incomplete tag labels. In this work, we develop a method for accurately clustering tagged videos based on a novel Hierarchical-Multi- Label Random Forest model capable of correlating structured visual and tag information. Specifically, our model exploits hierarchically structured tags of different abstractness of semantics and multiple tag statistical correlations, thus discovers more accurate semantic correlations among different video data, even with highly sparse/incomplete tags
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle
This paper initiates the study of active learning for exact recovery of
partitions exclusively through access to a same-cluster oracle in the presence
of bounded adversarial error. We first highlight a novel connection between
learning partitions and correlation clustering. Then we use this connection to
build a R\'enyi-Ulam style analytical framework for this problem, and prove
upper and lower bounds on its worst-case query complexity. Further, we bound
the expected performance of a relevant randomized algorithm. Finally, we study
the relationship between adaptivity and query complexity for this problem and
related variants.Comment: 28 pages, 2 figure
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
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