Abstract
The k-nearest neighbor (k-NN) graph is an important data structure for many data mining and machine learning applications. The accuracy of k-NN graphs depends on the object feature vectors, which are usually represented in high-dimensional spaces. Selecting the most important features is essential for providing compact object representations and for improving the graph accuracy. Having a compact feature vector can reduce the storage space and the computational complexity of search and learning tasks. In this paper, we propose NNWID-Descent, a similarity graph construction method that utilizes the NNF-Descent framework while integrating a new feature selection criterion, Support-Weighted Intrinsic Dimensionality, that estimates the contribution of each feature to the overall intrinsic dimensionality. Through extensive experiments on various datasets, we show that NNWID-Descent allows a significant amount of local feature vector sparsification while still preserving a reasonable level of graph accuracy.
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Acknowledgments
M.E. Houle acknowledges the financial support of JSPS Kakenhi Kiban (A) Research Grant 25240036 and JSPS Kakenhi Kiban (B) Research Grant 15H02753. V. Oria acknowledges the financial support of NSF Research Grant DGE 1565478.
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Houle, M.E., Oria, V., Wali, A.M. (2017). Improving k-NN Graph Accuracy Using Local Intrinsic Dimensionality. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds) Similarity Search and Applications. SISAP 2017. Lecture Notes in Computer Science(), vol 10609. Springer, Cham. https://doi.org/10.1007/978-3-319-68474-1_8
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DOI: https://doi.org/10.1007/978-3-319-68474-1_8
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