Normalized Compression Distance of Multisets with Applications

Cohen, Andrew R.; Vitányi, Paul M. B.

doi:10.1109/tpami.2014.2375175

Cited by 39 publications

(54 citation statements)

References 37 publications

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“…24 CNNs have been applied in many machine vision tasks, including biological image analysis. In other work, we have found approaches based on CNNs to obtain results comparable to the NCD, 8 and have also found the ability to improve the performance of the NCD by combining it with the subsequent neural network or other supervised machine learning algorithms. One key difference is that because the NCD is a metric distance, it enables direct comparisons of visual similarity among different image training sets.…”

Section: Discussionmentioning

confidence: 90%

“…The NCD uses file compression algorithms as a basis for approximating the relative Kolmogorov complexity. 8,9 Given a set of images X, the NCD of that is defined as…”

Section: Normalized Compression Distancementioning

confidence: 99%

“…This approach uses the Normalized Compression Distance (NCD). 8,9 The NCD is based on the notion of Kolmogorov complexity from the field Algorithmic Information Theory, 10 precisely quantifying the most concise description of the differences among a set of digital objects. The NCD is a normalized metric, meaning that it takes values on the range of [0,1], with 0 indicating that the digital objects are identical and 1 indicating that the digital objects are maximally dissimilar.…”

Section: Introductionmentioning

confidence: 99%

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Automated Measurement of Cobblestone Morphology for Characterizing Stem Cell Derived Retinal Pigment Epithelial Cell Cultures

Joshi

Mankowski

Winter

et al. 2016

Journal of Ocular Pharmacology and Therapeutics

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Purpose: Assessing the morphologic properties of cells in microscopy images is an important task to evaluate cell health, identity, and purity. Typically, subjective visual assessments are accomplished by an experienced researcher. This subjective human step makes transfer of the evaluation process from the laboratory to the cell manufacturing facility difficult and time consuming. Methods: Automated image analysis can provide rapid, objective measurements of cultured cells, greatly aiding manufacturing, regulatory, and research goals. Automated algorithms for classifying images based on appearance characteristics typically either extract features from the image and use those features for classification or use the images directly as input to the classification algorithm. In this study we have developed both feature and nonfeature extraction methods for automatically measuring ''cobblestone'' structure in human retinal pigment epithelial (RPE) cell cultures. Results: A new approach using image compression combined with a Kolmogorov complexity-based distance metric enables robust classification of microscopy images of RPE cell cultures. The automated measurements corroborate determinations made by experienced cell biologists. We have also developed an approach for using steerable wavelet filters for extracting features to characterize the individual cellular junctions. Conclusions: Two image analysis techniques enable robust and accurate characterization of the cobblestone morphology that is indicative of viable RPE cultures for therapeutic applications.

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Section: Discussionmentioning

confidence: 90%

“…The NCD uses file compression algorithms as a basis for approximating the relative Kolmogorov complexity. 8,9 Given a set of images X, the NCD of that is defined as…”

Section: Normalized Compression Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Measurement of Cobblestone Morphology for Characterizing Stem Cell Derived Retinal Pigment Epithelial Cell Cultures

Joshi

Mankowski

Winter

et al. 2016

Journal of Ocular Pharmacology and Therapeutics

Self Cite

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“…Among them two widely known measures are Normalized Information Distance (NID) [15] and the Compression Dissimilarity Measure (CDM) [12]. Whereas CDM works only with two objects, there is a generalisation of NID for multiple data objects [5]. However, here we are not interested in similarity, but in correlation.…”

Section: Related Workmentioning

confidence: 99%

Correlation by Compression

Budhathoki

Vreeken

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Discovering correlated variables is one of the core problems in data analysis. Many measures for correlation have been proposed, yet it is surprisingly ill-defined in general. That is, most, if not all, measures make very strong assumptions on the data distribution or type of dependency they can detect.In this work, we provide a general theory on correlation, without making any such assumptions. Simply put, we propose correlation by compression. To this end, we propose two correlation measures based on solid information theoretic foundations, i.e. Kolmogorov complexity. The proposed correlation measures possess interesting properties desirable for any sensible correlation measure. However, Kolmogorov complexity is not computable, and hence we propose practical and computable instantiations based on the Minimum Description Length (MDL) principle.In practice, we can apply the proposed measures on any type of data by instantiating them with any lossless real-world compressors that reward pairwise dependencies. Extensive experiments show that the correlation measures works well in practice, have high statistical power, and find meaningful correlations on binary data, while they are easily extendible to other data types.

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“…The popularity of the Internet and ease of access has motivated millions of users to input billions of words to create trillions of Web pages of, on average, low quality contents. Normalized Google Distance (NGD) is a semantic similarity measure derived from the number of hits returned by the Google search engine for a given set of keywords [6].…”

Section: Introductionmentioning

confidence: 99%