Hierarchical Clustering via Multi-View Constraint Satisfaction


Hierarchical Clustering via Multi-View Constraint Satisfaction – The problem of clustering from multi-view constraints using multi-view constraints is a fundamental problem in many research areas. While some researchers have studied it using multi-view constraint models, in others it has been used to learn an abstract constraint representation from multi-view constraint knowledge. In this paper, we propose a novel method for learning multi-view constraint representations based on the hierarchical clustering of multiple constraints. Our algorithm is based on a novel method for constructing constraints from multi-view constraint model embeddings and combining the resulting embeddings with the given constraint. We use multiple constraints, given as a set of constraint embeddings, in a multi-view constraint space as both feature vectors and constraint matrices. Extensive experiments show that our algorithm achieves state of the art performance on both synthetic and real datasets. Furthermore, the performance of our algorithm is comparable to multi-view constraint learning (MILE) when the context is restricted to the constraints, and can increase to more restrictive constraints.

In this work, we propose a new method for Non-linear Dimensionality Reduction (NDR) using an approximate representation of the output space. Our method combines two main characteristics and generalizes well to the real world: (1) the dimension of the model is increased and the number of parameters is decreased; (2) the objective is not directly related to the input dimension or the nonlinearity. To overcome these problems, we extend the Nystrond method from Nystrond-Nystrond to Bayesian Bayesian Networks for non-negative matrix. We show how to use the Nystrond method in NDR by solving a well studied non-negative matrix optimization problem. Our empirical results indicate that our method substantially improves the quality of the real state-of-the-art non-negative matrix optimization and that its use in the Bayesian framework can also be interpreted as a useful tool for improving state-of-the-art optimization methods.

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Hierarchical Clustering via Multi-View Constraint Satisfaction

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    Linearity Estimation for Deep Neural Networks via Non-linear Dimensionality ReductionIn this work, we propose a new method for Non-linear Dimensionality Reduction (NDR) using an approximate representation of the output space. Our method combines two main characteristics and generalizes well to the real world: (1) the dimension of the model is increased and the number of parameters is decreased; (2) the objective is not directly related to the input dimension or the nonlinearity. To overcome these problems, we extend the Nystrond method from Nystrond-Nystrond to Bayesian Bayesian Networks for non-negative matrix. We show how to use the Nystrond method in NDR by solving a well studied non-negative matrix optimization problem. Our empirical results indicate that our method substantially improves the quality of the real state-of-the-art non-negative matrix optimization and that its use in the Bayesian framework can also be interpreted as a useful tool for improving state-of-the-art optimization methods.


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