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NlogN Entropy Optimization for Efficient Estimation & Gradient Computation

Explore NlogN entropy optimization for accurate PDF estimation, efficient entropy gradient computation, and super ICA performance. Enhance your data analysis with advanced kernel estimators and convolution techniques.

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NlogN Entropy Optimization for Efficient Estimation & Gradient Computation

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  1. NlogN Entropy Optimization 1 Sarit Shwartz Yoav Y. Schechner Michael Zibulevsky Sponsors: ISF, Dvorah Foundation

  2. 49 Kernel Estimators: Parzen Windows Estimated PDF Data True PDF Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  3. Previous Work Parametric PDF:Hyvärinen 98, Bell; Sejnowski 95, Pham; Garrat 97. Cumulants:Cardoso ; Souloumiac 93. Not accurate

  4. Order statistics:Vasicek 76, Learned-Miller; Fisher 03. KD trees:Gray; Moore 03. Previous Work Not differentiable

  5. Entropy Estimation Kernel Estimators: reduced complexityPham, 03, .Erdogmus; Principe; Hild, 03, Morejon; Principe 04,Schraudolph 04, (Stochastic gradient).

  6. Source Range: Continuous

  7. 50 Parzen Windows Estimator Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  8. 51 Minimization of Mutual Information Differentiable Computationally efficient - Currently O(K N) online code (see website) 2 2 Independent Component Analysis Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  9. Parzen Windows as a Convolution 52 Wish it was … Discrete convolution Convolution Sampling Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  10. 53 Efficient Kernel Estimator Samples of estimated sources PDF estimation Fan; Marron 94, Silverman 82. A Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  11. 53 Samples of estimated sources Interpolation to uniform grid (histogram) PDF estimation Fan; Marron 94, Silverman 82. Efficient Kernel Estimator A B Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  12. 53 Samples of estimated sources Interpolation to uniform grid(histogram) Discrete convolution with Parzen window C PDF estimation Fan; Marron 94, Silverman 82. Efficient Kernel Estimator A B Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  13. 54 Efficient Entropy Estimator C D A • Interpolation to original values Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  14. 55 Can it be Used for Optimization? • Iterations exploiting derivatives of . W separate Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  15. 56 Can it be Used for Optimization? W separate • Binning fluctuations of . • Fluctuations amplified by differentiation. • Fluctuations slow convergence, false minima. Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  16. 57 Quantization and Optimization Function Quantized function Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  17. 57 Quantization and Optimization Function Quantized function Function with a quantized derivative Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  18. Analytic Entropy Gradient Accurate derivative Efficient calculation Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  19. Analytic Entropy Gradient Complexity K- number of sources, N-data length. Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  20. 58 Entropy Gradient by Convolutions Convolution Convolution Convolution Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  21. 59 Entropy Gradient by Convolutions • Calculation of using convolutions. • Approximation of convolutions with complexity. • Distinct quantization of the derivative. Not differentiation of a quantized function. Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

  22. 60 Super ICA performance Non-parametric algorithms. Parametric algorithms. K=6 random sources, N= 3000 samples. Shwartz, Schechner & Zibulevsky, NlogN entropy optimization

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