J. McNames. A fast nearest-neighbor algorithm based on a principal axis search tree. PAMI, 23(9):964-976, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....search algorithm that combines optimal depth first branch and bound with a new path ordering and pruning method. Many other recursive subdivision based techniques have also been proposed for the kNN and AkNN problems, including kd B trees [55] BBD trees [7] BAR trees [16] Principal Axis Trees [47], the R tree family of data structures, 40,58] and ANN trees [44] Unfortunately, all schemes based on recursive search over a tree share the same memory dependency problem as the kd tree. Graph based Techniques The second category of techniques are based on building and searching graphs that ....

J. McNames. A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):964-- 976, 2001. 2.2

....neighbors is called the elimination rule and is based on a lower bound distance between a vector and a node. In [18] the author gives a theoretic derivation for an efficient decomposition method based on principal component analysis of which the efficiency was experimentally shown previously in [19]. Current research focuses on a quantitative comparison of different decomposition methods and elimination rules. Expressing the Physical Constrains A disadvantage of the pattern recognition approach is that no constraints are imposed on the control parameters of the physical model resulting in ....

James McNames, "A fast nearest neighbor algorithm based on a principal axis search tree," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 964--976, September 2001.

....is large however, the computational cost of the nearest neighbor search can prohibit its practical use. Various techniques that reduce the number of distance computations have been proposed. For data that can be represented in a vector space, branch and bound search algorithms have been proposed [2, 3, 4, 5, 6]. For nearest neighbors in a metrical space, several approximating and eliminating search algorithms (AESA) are available [7, 8, 9, 10, 11] In the present work, a branch and bound algorithm is proposed that searches the nearest vectors in a vector space where the dissimilarity between two ....

....with unit covariance matrix provide therefore a worst case estimation of the efficiency. During the revision process of this paper, a recent article was brought to the attention of the author in which a branch and bound algorithm was proposed with exactly the same decomposition method [6]. This algorithm was called the Principal 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 computation time (s) Figure 5: average computation time for D =2. 10 11 12 13 14 15 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 computation time (s) Figure 6: average ....

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James McNames, "A fast nearest neighbor algorithm based on a principal axis search tree," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 964--976, september 2001.

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J. McNames. A fast nearest-neighbor algorithm based on a principal axis search tree. PAMI, 23(9):964-976, 2001.

No context found.

James McNames, "A fast nearest neighbor algorithm based on a principal axis search tree," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 964--976, September 2001.

No context found.

J. McNames. A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):964--976, 2001. 2

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