Adaptive Sampling for High Throughput Data Using Similarity Measures
The need for adaptive sampling arises in the context of high throughput data because the rates of data arrival are many orders of magnitude larger than the rates at which they can be analyzed. A very fast decision must therefore be made regarding the value of each incoming observation and its inclusion in the analysis. In this report we discuss one approach to adaptive sampling, based on the new data point’s similarity to the other data points being considered for inclusion. We present preliminary results for one real and one synthetic data set.
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- Resource Type:
- Technical Report
- Research Org:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
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- Country of Publication:
- United States
- 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
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