Abstract
Modeling data sets as points in a high dimensional vector space is a trendy theme in modern information retrieval and data mining. Among the numerous drawbacks of this approach is the fact that many of the required processing tasks are computationally hard in high dimension. We survey several algorithmic ideas that have applications to the design and analysis of polynomial time approximation schemes for nearest neighbor search and clustering of high dimensional data. The main lesson from this line of research is that if one is willing to settle for approximate solutions, then high dimensional geometry is easy. Examples are included in the reference list below.
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Rabani, Y. (2002). Search and Classification of High Dimensional Data. In: Jansen, K., Leonardi, S., Vazirani, V. (eds) Approximation Algorithms for Combinatorial Optimization. APPROX 2002. Lecture Notes in Computer Science, vol 2462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45753-4_1
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DOI: https://doi.org/10.1007/3-540-45753-4_1
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