Algorithmic Information Theoretic Prediction & Discovery
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Our approach uses algorithmic information theory to analyze results from accurately segmenting and tracking objects in the image sequences. Multi-target tracking is used to establish temporal correspondences between the segmented objects. A multi-resolution algorithmic information theoretic distance measure computes pairwise comparisons between time courses of object feature values. This results in an ensemble of distance matrices that are further analyzed using a class of semi-supervised spectral learning techniques. Our approach is semi-supervised. If final outcomes are unknown, it functions as a "tool for discovery," and uses a clustering model based on the notion of randomness deficiency from algorithmic statistics to capture as much meaningful information from image sequence data as possible. If final outcomes are known it becomes semi-supervised, incorporating both labeled and unlabeled data for the pair-wise comparisons and then using cross validation together with supervised spectral learning for the most accurate predictions based on behavior. | Our approach uses algorithmic information theory to analyze results from accurately segmenting and tracking objects in the image sequences. Multi-target tracking is used to establish temporal correspondences between the segmented objects. A multi-resolution algorithmic information theoretic distance measure computes pairwise comparisons between time courses of object feature values. This results in an ensemble of distance matrices that are further analyzed using a class of semi-supervised spectral learning techniques. Our approach is semi-supervised. If final outcomes are unknown, it functions as a "tool for discovery," and uses a clustering model based on the notion of randomness deficiency from algorithmic statistics to capture as much meaningful information from image sequence data as possible. If final outcomes are known it becomes semi-supervised, incorporating both labeled and unlabeled data for the pair-wise comparisons and then using cross validation together with supervised spectral learning for the most accurate predictions based on behavior. | ||
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Revision as of 15:23, 24 April 2009
Our approach uses algorithmic information theory to analyze results from accurately segmenting and tracking objects in the image sequences. Multi-target tracking is used to establish temporal correspondences between the segmented objects. A multi-resolution algorithmic information theoretic distance measure computes pairwise comparisons between time courses of object feature values. This results in an ensemble of distance matrices that are further analyzed using a class of semi-supervised spectral learning techniques. Our approach is semi-supervised. If final outcomes are unknown, it functions as a "tool for discovery," and uses a clustering model based on the notion of randomness deficiency from algorithmic statistics to capture as much meaningful information from image sequence data as possible. If final outcomes are known it becomes semi-supervised, incorporating both labeled and unlabeled data for the pair-wise comparisons and then using cross validation together with supervised spectral learning for the most accurate predictions based on behavior.
