Cause and effect data analysis represents changes in observeddata in terms of changes in the causal factors . Counterfactual approach performs an intervention on the model of data formation . We propose a unified multilinear model of wholes and parts . The resulting object representation is aninterpretable combinatorial choice of intrinsic causal factor representations that renders object recognition robust to occlusion and reduces training data requirements. This incremental computational approach may also be employed to update the causal model parameters when data becomes available incrementally. The resulting result is an interpretable choice of the intrinsic causal factors of an object that rendersobject recognition robust and reduces train data requirements and increases training data needs to be updated to reflect the model. The Incremental M-mode Block SVD is based on an incremental computational approaches to the causal models. The incremental approach mayalso be used to update model parameters in the model’s causal models when data becomeavailable incrementally, according to the appropriate data becomesavailable incrementately. The M- Mode Block Block Svd is a tool that employs the lower-levelabstractions, to represent the higher level of abstractions, the parent wholes.

Author(s) : M. Alex O. Vasilescu, Eric Kim, Xiao S. Zeng

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Keywords : data - causal - model - incremental - block -

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