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Isolation of Root Causes Correlated with Anomalous Production Variances

Isolation of Root Causes Correlated with Anomalous Production Variances. Joshua V. Dillon jvdillon@purdue.edu. Unsupervised learning of “weird” sequencing results Correlate that knowledge with paths in production Ultimately treated these as two distinct problems. Goal.

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Isolation of Root Causes Correlated with Anomalous Production Variances

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  1. Isolation of Root Causes Correlatedwith Anomalous Production Variances Joshua V. Dillon jvdillon@purdue.edu

  2. Unsupervised learning of “weird” sequencing results Correlate that knowledge with paths in production Ultimately treated these as two distinct problems Goal

  3. Principal Component Analysis • Dimensionality reduction • Optimal linear transform • Choice of information energy • Covariance/ Correlation • In short: math.

  4. Variant of expectation-maximization Minimizes total intra-cluster variance Fast convergence k-Centroids Clustering

  5. k-Farthest Neighbor Clustering • Minimize (Euclidean) “dissimilarity” • Sensitive to noise (fix: scale by entropy) • Computationally intensive

  6. Kernel density estimation Extrapolates sample to entire population (above) Z-score scaled to unit hyper-cube and convolved with unimodal Gaussian Parzen Windowing

  7. “distance” from distribution P to arbitrary distribution Q Non-negative, asymmetric Equal to cross-entropy of P and Q minus entropy of P;“uncertainty between P and Q minus uncertainty of P” KL-divergence

  8. Two words: Brute. Force. aka “math is hard” Permute all values of all columns and take the mean of plates sharing those traits Where the mean is conditioned upon the mutual information shared in the learned trace anomalies Sounds great! But first, the woes of being a brute… What Now?

  9. Combinatorial problem; NP-hard Heuristics: biggest first, best fit, etc Knapsack problem Uggh. Biggest First Bin-Packing

  10. Global search heuristic; monte carlo method Binary (canonical) encoding of chromosomes indicating bin membership Seeded by BF-bin packing 20 chromosomes, 90% chance of crossover, 0.1% chance of mutation Genetic Algorithm Bin-Packing

  11. Memory-mapped I/O • Permute, store indices, compute mean, sort • Simply not enough swap space • Map memory onto hard disk • Make kswapd suffer—you suffer • Initial tests indicate still faster than competitors

  12. 3x3 Sobel filter Discrete differentiation operator Approximates gradient Search based method (1st derivative) Zero-crossing (ie Laplacian) use 2nd derivative Bonus: Edge Detection

  13. Isolation of Root Causes Correlatedwith Anomalous Production Variances Joshua V. Dillon jvdillon@purdue.edu

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