Predicting from aggregated data

Aggregated data, which refers to a collection of data summarized from multiple sources, is a technique commonly used in different fields of research including healthcare, web application, and sensor network. Aggregated data is often employed to handle issues such as privacy, scalability, and reliability. However, accurately predicting individual outcomes from grouped datasets can be very difficult. In this thesis, we designed a new learning method, a Mixture of Expert (MoE) model, focused on individual-level prediction when training variables are aggregated. We utilized the MoE model, trained and validated using the eICU Collaborative Research patient datasets, to conduct a series of studies. Our results showed that applying grouping functions to the classification of aggregated data across demographic and behavior metrics could remain effective. This technique was verified by comparing two separately trained MoE models that were evaluated on the same datasets. Finally, we estimated non-aggregated datasets from spatio-temporal aggregated records by expressing the problem into the frequency domain, and trained an autoregressive model for predicting future stock prices. This process can be repeated, offering a potential solution to the issue of learning from aggregated data.Ope

Good exemplars of natural scene categories elicit clearer patterns than bad exemplars but not greater BOLD activity

Within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. We used ‘good’ and ‘bad’ exemplars of six natural scene categories to confirm that human categorization is sensitive to this manipulation and explore whether brain regions previously implicated in natural scene categorization show a similar sensitivity to how well an image exemplifies a category. Participants were more accurate and faster at categorizing good exemplars of natural scenes. A classifier trained to discriminate patterns of fMRI activity associated with the viewing of our scene categories showed higher decoding accuracy for good than bad exemplars of a category in the PPA, RSC and V1. A univariate analysis, however, revealed that there was either no difference in overall BOLD signal evoked by good and bad scenes (in RSC and V1) or the signal was actually higher for bad scenes (in PPA), suggesting that good exemplars produce a qualitatively, rather than quantitatively, better pattern of activity for categorizing natural scenes. Overall, our results provide further evidence that V1, RSC and the PPA contain information relevant for natural scene categorization. Finally, image statistic analysis shows that good images in our categories produce a more discernible average image and are more similar to each other. These results are consistent with both low-level models of scene category and models in which the category is built around a prototype