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, high conditional probabilities) with many other outlying values.
This enables CBRW to distinct outlying values from noisy feature values, as noisy values are supposed to have weak couplings with outlying values.
For example, compared to the noisy value 'bachelor', although the outlying value 'low' has lower outlierness by only con- sidering intra-feature value couplings, it has much higher out- lierness when adding inter-feature value couplings, because it has stronger couplings with the exceptional value 'divorced'.
occurs with many outlying values, since ⇡⇤(v) is proportional
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