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Pattern recognition systems based on compressed patterns and compressed sensor measurements can be designed using low-density matrices. The authors examine truncation encoding where a subset of the patterns and measurements are stored perfrectly while the rest is discarded. They also examine the use of LDPC parity check matrices for compressing measurements and patterns. They show how more general ensembles of good linear codes can be used as the basis for pattern recognition system design, yielding system design strategies for more general noise models.
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