Chapter20_TAMU.pdf - ECEN 489 Information Theory Inference and Learning Algorithms Chapter 20 An Example Inference Task Clustering Dr Chao TIAN Texas

Chapter20_TAMU.pdf - ECEN 489 Information Theory Inference...

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ECEN 489: Information Theory, Inference, and Learning Algorithms Chapter 20: An Example Inference Task: Clustering Dr. Chao TIAN Texas A&M University 1 / 11
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Clustering What is clustering: classification, grouping, etc.: Put things into different categories; Humans are good at this: animal vs. plants, bitter food vs. sweet food; More generally: N objects need to be grouped into K clusters. Why do clustering? Good prediction power: animal vs. plant Lossy compression (conceptualization); Find outliers that need special attention; Models of learning processes in neural systems. 2 / 11
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Connection to Quantization Quantization: from data to its representation Assignment rule: k ( ), assign to the k -th codeword; Reconstruction rule: k ( k ) . Clustering: Clustering assignment: assign to k -th cluster; Representative symbol: ( k ) . 3 / 11
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