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» Introduction to Randomized Algorithms
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SDM
2007
SIAM
98views Data Mining» more  SDM 2007»
15 years 4 months ago
An incremental data-stream sketch using sparse random projections
We propose the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch al...
Aditya Krishna Menon, Gia Vinh Anh Pham, Sanjay Ch...
CSDA
2007
100views more  CSDA 2007»
15 years 3 months ago
Convergence of random k-nearest-neighbour imputation
Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missing...
Fredrik A. Dahl
COMBINATORICS
2000
88views more  COMBINATORICS 2000»
15 years 2 months ago
Note on Sparse Random Graphs and Cover Graphs
It is shown in this note that with high probability it is enough to destroy all triangles in order to get a cover graph from a random graph Gn,p with p log n/n for any constant ...
Tom Bohman, Alan M. Frieze, Miklós Ruszink&...
ATS
2000
IEEE
98views Hardware» more  ATS 2000»
15 years 7 months ago
Embedded core testing using genetic algorithms
Testing of embedded cores is very difficult in SOC (system-on-a-chip), since the core user may not know the gate level implementation of the core, and the controllability and obse...
Ruofan Xu, Michael S. Hsiao
CORR
2010
Springer
320views Education» more  CORR 2010»
15 years 3 months ago
An algorithm for the principal component analysis of large data sets
Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy -- even on parallel processors -- unlike the...
Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnis...