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Probabilistic self-organizing maps for qualitative data

Probabilistic self-organizing maps for qualitative data. Ezequiel Lopez-Rubio NN, Vol.23 2010, pp. 1208–1225 Presenter : Wei- Shen Tai 20 10 / 11/17. Outline. Introduction Basic concepts The model Experimental results Conclusions Comments. Motivation.

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Probabilistic self-organizing maps for qualitative data

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  1. Probabilistic self-organizing maps for qualitative data Ezequiel Lopez-Rubio NN, Vol.23 2010, pp. 1208–1225 Presenter : Wei-Shen Tai 2010/11/17

  2. Outline • Introduction • Basic concepts • The model • Experimental results • Conclusions • Comments

  3. Motivation • Non-continuous data in self-organization map • Re-codify the categorical data to fit the existing continuous valued models (1-of-k coding )or impose a distance measure on the possible values of a qualitative variable (distance hierarchy). • SOM depends heavily on the possibility of adding and subtracting input vectors, and on a proper distance measure among them.

  4. Objective • A probability-based SOM • Without the need of any distance measure between the values of the input variables.

  5. Chow–Liu algorithm • Obtain the maximum mutual information spanning tree. • Compute the probability of input x belonged to the tree.

  6. Robbins–Monro algorithm • A stochastic approximation algorithm • Its goal is to find the value of some parameter τ which satisfies • A random variable Y which is a noisy estimate of ζ • This algorithm proceeds iteratively to obtain a running estimation θ (t) of the unknown parameter τ • where ε(t) is a suitable step size. (similar to LR(t) in SOM)

  7. Map and units • Map definition • Each mixture component iis associated with a unit in the map. • Structure of the units

  8. Self-organization • Find BMU • Learning method

  9. Initialization and summary • Initialization of the map • Summary • 1. Set the initial values for all mixture components i. • 2. Obtain the winner unit of an input xt and the posterior responsibilities Rti of the winner. • 3. For every component i, estimate its parameters πi(t),ψijh(t) and ξijhks(t). • 4. Compute the optimal spanning tree of each component. • 5. If the map has converged or the maximum time step T has been reached, stop. Otherwise, go to step 2.

  10. Experimental results • Cars in three graphic results

  11. Quality measures

  12. Conclusion • A probabilistic self-organizing map model • Learns from qualitative data which do not allow meaningful distance measures between values.

  13. Comments • Advantage • This proposed model can handle categorical data without distance measure between units (neurons) and inputs during the training . • That is, categorical data are handled by mapping probability instead of 1-of-k coding and distance hierarchy in this model. • Drawback • The size of weight vector will explosively grow as the number of categorical attributes and their possible values. That makes these computational processes become complex as well. • It fits for categorical data but mixed-type data. • Application • Categorical data in SOMs.

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