Algorithm 777: HOMPACK90: a suite of Fortran 90 codes for globally convergent homotopy algorithms
ACM Transactions on Mathematical Software (TOMS)
Parallel Homotopy Curve Tracking on a Hypercube
Proceedings of the Fourth SIAM Conference on Parallel Processing for Scientific Computing
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Semisupervised Learning of Hidden Markov Models via a Homotopy Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heterogeneous transfer learning for image clustering via the social web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A metric-based framework for automatic taxonomy induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A discriminative model for semi-supervised learning
Journal of the ACM (JACM)
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
Continuation methods for mixing heterogeneous sources
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Modern machine learning problems typically have multiple criteria, but there is currently no systematic mathematical theory to guide the design of formulations and exploration of alternatives. Homotopy methods are a promising approach to characterize solution spaces by smoothly tracking solutions from one formulation (typically an "easy" problem) to another (typically a "hard" problem). New results in constructing homotopy maps for constrained clustering problems are here presented, which combine quadratic loss functions with discrete evaluations of constraint violations are presented. These maps balance requirements of locality in clusters as well as those of discrete must-link and must-not-link constraints. Experimental results demonstrate advantages in tracking solutions compared to state-of-the-art constrained clustering algorithms.