Scalable Solution for Approximate Nearest Subspace Search
Masakazu Iwamura, Masataka Konishi, Koichi Kise

TL;DR
This paper introduces a scalable approximate nearest subspace search method that represents each subspace by multiple points, enabling efficient large-scale searches with high accuracy.
Contribution
The paper proposes a novel approach that represents subspaces by multiple points, significantly improving scalability and speed over existing methods.
Findings
7.3 times faster than previous state-of-the-art
Maintains accuracy while scaling to large datasets
Effective in high-dimensional spaces
Abstract
Finding the nearest subspace is a fundamental problem and influential to many applications. In particular, a scalable solution that is fast and accurate for a large problem has a great impact. The existing methods for the problem are, however, useless in a large-scale problem with a large number of subspaces and high dimensionality of the feature space. A cause is that they are designed based on the traditional idea to represent a subspace by a single point. In this paper, we propose a scalable solution for the approximate nearest subspace search (ANSS) problem. Intuitively, the proposed method represents a subspace by multiple points unlike the existing methods. This makes a large-scale ANSS problem tractable. In the experiment with 3036 subspaces in the 1024-dimensional space, we confirmed that the proposed method was 7.3 times faster than the previous state-of-the-art without loss of…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Human Pose and Action Recognition
