Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

Sally Ghanem; Hamid Krim; Hamilton Scott Clouse; Wesam Sakla

doi:10.1109/TIP.2018.2862352

Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

Sally Ghanem, Hamid Krim, Hamilton Scott Clouse, Wesam Sakla

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.

Original language	English
Article number	8424176
Pages (from-to)	5947-5956
Number of pages	10
Journal	IEEE Transactions on Image Processing
Volume	27
Issue number	12
DOIs	https://doi.org/10.1109/TIP.2018.2862352
State	Published - Dec 2018
Externally published	Yes

Keywords

Dimensionality reduction
classification
divergence measures
nearest neighbor graph
pattern recognition

Access to Document

10.1109/TIP.2018.2862352

Cite this

@article{bb68f60089bc4131a4ccbd1662d7a63a,

title = "Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic",

abstract = "Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.",

keywords = "Dimensionality reduction, classification, divergence measures, nearest neighbor graph, pattern recognition",

author = "Sally Ghanem and Hamid Krim and Clouse, \{Hamilton Scott\} and Wesam Sakla",

note = "Publisher Copyright: {\textcopyright} 1992-2012 IEEE.",

year = "2018",

month = dec,

doi = "10.1109/TIP.2018.2862352",

language = "English",

volume = "27",

pages = "5947--5956",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers",

number = "12",

}

TY - JOUR

T1 - Metric Driven Classification

T2 - A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

AU - Ghanem, Sally

AU - Krim, Hamid

AU - Clouse, Hamilton Scott

AU - Sakla, Wesam

PY - 2018/12

Y1 - 2018/12

N2 - Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.

AB - Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.

KW - Dimensionality reduction

KW - classification

KW - divergence measures

KW - nearest neighbor graph

KW - pattern recognition

UR - https://www.scopus.com/pages/publications/85050971273

U2 - 10.1109/TIP.2018.2862352

DO - 10.1109/TIP.2018.2862352

M3 - Article

C2 - 30072326

AN - SCOPUS:85050971273

SN - 1057-7149

VL - 27

SP - 5947

EP - 5956

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

IS - 12

M1 - 8424176

ER -

Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this