Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Redundant bit vectors for quickly searching high-dimensional regions
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Nearest neighbor search is one of the most fundamental problem in machine learning, machine vision, clustering, information retrieval, etc. To handle a dataset of million or more records, efficient storing and retrieval techniques are needed. Binary code is an efficient method to address these two problems. Recently, the problem of finding good binary code has been formulated and solved, resulting in a technique called spectral hashing [21]. In this work we analyze the spectral hashing, its possible shortcomings and solutions. Experimental results are promising.