Temporal Multinomial Mixture (TMM) google
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering. …

Sequential Floating Forward Selection (SFFS) google
The Sequential Floating Forward Selection (SFFS) algorithm can be considered as extension of the simpler Sequential Fortward Selection (SFS) algorithm. In constrast to SFS, the SFFS algorithm can remove features once they were included, so that a larger number of feature subset combinations can be sampled. It is important to emphasize that the removal of included features is conditional, which makes it different from the +L -R algorithm. The Conditional Exclusion in SFFS only occurs if the resulting feature subset is assessed as “better” by the criterion function after removal of a particular feature. …

Self-Paced Learning (SPL) google
It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this work, we present a new way to robustify AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning (SPL) into Boosting framework. Specifically, we design a new robust Boosting algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented by slightly modifying off-the-shelf Boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost. …

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