document: Distributed multivariate regression based on influential observations Large-scale data sets are sometimes logically and physically distributed in separate databases . The issues of mining these data sets are not just their sizes , but also the distributed nature . The complication is that communicating all the data to a central database would be too slow . To reduce communication costs , one could compress the data during transmission . Another method is random sampling . We propose an approach for distributed multivariate regression based on sampling and discuss its relationship with the compression method . The central idea is motivated by the observation that , although communication is limited , each individual site can still scan and process all the data it holds . Thus it is possible for the site to communicate only influential samples without seeing data in other sites . We exploit this observation and derive a method that provides tradeoff between communication cost and accuracy . Experimental results show that it is better than the compression method and random sampling .
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