We introduce a new variant of the nearest neighbor search problem, which allows for some coordinates of the dataset to be arbitrarily corrupted or unknown. Formally, given a dataset of n points P={x1,…,xn} in high-dimensions, and a parameter k, the goal is to preprocess the dataset, such that given a query point q, one can compute quickly a point x∈P, such that the distance of the query to the point x is minimized, when ignoring the ``optimal'' k coordinates. Note, that the coordinates being ignored are a function of both the query point and the point returned.

We present a general reduction from this problem to answering \ANN queries, which is similar in spirit to \LSH (locality sensitive hashing). Specifically, we give a sampling technique which achieves a bi-criterion approximation for this problem. If the distance to the nearest neighbor after ignoring k
coordinates is r, the data-structure returns a point that is within a distance of O(r) after ignoring O(k)
coordinates. We also present other applications and further extensions and refinements of the above result.

The new data-structures are simple and (arguably) elegant, and should be practical -- specifically, all bounds are polynomial in all relevant parameters (including the dimension of the space, and the robustness parameter k
).

Joint work with Sepideh Mahabadi.