Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, LLNL scientists developed function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. FPP constructs 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns of the response functions. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in large-scale data that could not be achieved using existing dimension reduction methods.
FPP, Open-sourced software licensed under the BSD license (LLNL internal case # CP02219)