Dimensionality Invariant Similarity Measure

@article{Hassanat2014DimensionalityIS,
  title={Dimensionality Invariant Similarity Measure},
  author={Ahmad Basheer Hassanat},
  journal={CoRR},
  year={2014},
  volume={abs/1409.0923}
}
This paper presents a new similarity measure to be used for general tasks including supervised learning, which is represented by the K-nearest neighbor classifier (KNN). The proposed similarity measure is invariant to large differences in some dimensions in the feature space. The proposed metric is proved mathematically to be a metric. To test its viability for different applications, the KNN used the proposed metric for classifying test examples chosen from a number of real datasets. Compared… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 25 references

A Comparative Study for Comparing Two Feature Extraction Methods and Two Classifiers in Classification of Earlystage Lung Cancer Diagnosis of chest x-ray images.

  • Al Gindi, M Amal, Tawfik A Attiatalla, Moustafa M Sami
  • J Am Sci 10,
  • 2014
Highly Influential
10 Excerpts

Pattern Matching using Similarity Measures

  • M. Hagedoorn
  • PhD. Thesis, Universiteit Utrecht,
  • 2000
Highly Influential
3 Excerpts

Comparison of Similarity Metrics for Thumbnail Based Image Retrieval.

  • Khapli, R Vidya, Anjali S Bhalchandra
  • JOURNAL OF COMPUTER SCIENCE AND ENGINEERING 5,
  • 2011
1 Excerpt

Performance Evaluation of Distance Metrics: Application to Fingerprint Recognition.

  • Bharkad, D Sangita, Manesh Kokare
  • International Journal of Pattern Recognition and…
  • 2011
3 Excerpts

Encyclopedia of Distances

  • M Deza, E Deza
  • 2009
2 Excerpts

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