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Quantifying Intermembrane Distances with Serial Image Dilations

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Normalized Compression Distance of Multisets with Applications.

Andrew R Cohen, Paul M B Vitányi

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This summary is machine-generated.

We introduce a new metric for multisets, a normalized compression distance (NCD), that improves classification accuracy over pairwise NCD. This novel approach offers a more efficient and effective method for data analysis in various fields.

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Area of Science:

Information theory
Computer science
Data mining

Background:

Normalized compression distance (NCD) is a parameter-free, feature-free, and alignment-free similarity metric.
Previous attempts to create a metric NCD for multisets were unsuccessful.

Purpose of the Study:

To propose a novel metric NCD for multisets.
To demonstrate its superiority in classification tasks compared to pairwise NCD.
To provide a theoretical foundation and practical implementation.

Main Methods:

Developing a new NCD for multisets based on Kolmogorov complexity.
Applying the new NCD to biological datasets (stem cell, organelle transport) and OCR classification.
Comparing its performance against the established pairwise NCD.

Main Results:

The proposed NCD for multisets is a valid metric.
It significantly outperforms pairwise NCD in classification accuracy.
It also offers improved implementation complexity.

Conclusions:

The new NCD for multisets provides a more accurate and efficient classification method.
This advancement has broad applicability in biological and OCR data analysis.
The theoretical underpinnings in Kolmogorov complexity support its effectiveness.