Selective sampling with a hierarchical latent variable model

We present a new method which combines a hierarchical stochastic latent variable model and a selective sampling strategy, for learning from co-occurrence events, i.e. a fundamental issue in intelligent data analysis. The hierarchical stochastic latent variable model we employ enables us to use existing background knowledge of observable co-occurrence events as a latent variable. The selective sampling strategy we use iterates selecting plausible non-noise examples from a given data set and running the learning of a component stochastic model alternately and then improves the predictive performance of a component model. Combining the model and the strategy is expected to be effective for enhancing the performance of learning from real-world co-occurrence events. We have empirically tested the performance of our method using a real data set of protein-protein interactions, a typical data set of co-occurrence events. The experimental results have shown that the presented methodology significantly outperformed an existing approach and other machine learning methods compared, and that the presented method is highly effective for unsupervised learning from co-occurrence events.