Final Report Summary - MININEXACT (Exact Mining from In-Exact Data)
We have studied the following problems:
1) Fast data compression that distorts to the last amount the outcome of machine learning and database operations.
2) Joint right-protection and anonymization of data, with provable guarantees on the data utility.
3) Leveraging ensembles of random projections to provide fast learning algorithms with probabilistic guarantees on the mining capacity of the distorted data.
4) Fast recommendation algorithms balance interpretability and accuracy of the prediction models on noisy data.