Abstract
We study the problem of computing low-distortion embeddings in the streaming model. We present streaming algorithms that, given an n-point metric space M, compute an embedding of M into an n-point metric space M′ that preserves a (1 − σ)-fraction of the distances with small distortion (σ is called the slack). Our algorithms use space polylogarithmic in n and the spread of the metric. Within such space limitations, it is impossible to store the embedding explicitly. We bypass this obstacle by computing a compact representation of M′, without storing the actual bijection from M into M′.
Partially supported by DFG grant So 514/1-2.
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Lammersen, C., Sidiropoulos, A., Sohler, C. (2009). Streaming Embeddings with Slack. In: Dehne, F., Gavrilova, M., Sack, JR., Tóth , C.D. (eds) Algorithms and Data Structures. WADS 2009. Lecture Notes in Computer Science, vol 5664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03367-4_42
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DOI: https://doi.org/10.1007/978-3-642-03367-4_42
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