  Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a
critical yet challenging problem. In AKNN search, distance computation is the
core task that dominates the runtime. Existing approaches typically use
approximate distances to improve computational efficiency, often at the cost of
reduced search accuracy. To address this issue, the state-of-the-art method,
ADSampling, employs random projections to estimate approximate distances and
introduces an additional distance correction process to mitigate accuracy loss.
However, ADSampling has limitations in both effectiveness and generality,
primarily due to its reliance on random projections for distance approximation
and correction. To address the effectiveness limitations of ADSampling, we
leverage data distribution to improve distance computation via orthogonal
projection. Furthermore, to overcome the generality limitations of ADSampling,
we adopt a data-driven approach to distance correction, decoupling the
correction process from the distance approximation process. Extensive
experiments demonstrate the superiority and effectiveness of our method. In
particular, compared to ADSampling, our method achieves a speedup of 1.6 to 2.1
times on real-world datasets while providing higher accuracy.
