论文信息 - The Influence of Hubness on NN-Descent

The Influence of Hubness on NN-Descent

The K-nearest neighbor graph (K-NNG) is a data structure used by many machine-learning algorithms. Naive computation of the K-NNG has quadratic time complexity, which in many cases is not efficient...

[1] Hanan Samet,et al. A fast all nearest neighbor algorithm for applications involving large point-clouds , 2007, Comput. Graph..

[2] M. R. Brito,et al. Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection , 1997 .

[3] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[4] Christos Faloutsos,et al. The TV-tree: An index structure for high-dimensional data , 1994, The VLDB Journal.

[5] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[6] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7] C. Lanczos. An iteration method for the solution of the eigenvalue problem of linear differential and integral operators , 1950 .

[8] Alexandros Nanopoulos,et al. Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection , 2015, IEEE Transactions on Knowledge and Data Engineering.

[9] Piyush Kumar,et al. Fast construction of k-nearest neighbor graphs for point clouds , 2010, IEEE Transactions on Visualization and Computer Graphics.