Neighborhood preserving ordinal regression

Yang Liu; Yan Liiu; Keith C.C. Chan; Jing Zhang

doi:10.1145/2382336.2382370

Neighborhood preserving ordinal regression

Yang Liu^*
, Yan Liiu
, Keith C.C. Chan
, Jing Zhang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

8 Citations (Scopus)

Abstract

Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	ICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Pages	119-122
Number of pages	4
DOIs	https://doi.org/10.1145/2382336.2382370
Publication status	Published - Sept 2012
Event	4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012 - Wuhan, China Duration: 9 Sept 2012 → 11 Sept 2012 https://dl.acm.org/doi/proceedings/10.1145/2382336 (Conference proceeding)

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012
Abbreviated title	ICIMCS '12
Country/Territory	China
City	Wuhan
Period	9/09/12 → 11/09/12
Internet address	https://dl.acm.org/doi/proceedings/10.1145/2382336 (Conference proceeding)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

Manifold learning
Maximum margin
Neighborhood Preserving Ordinal Regression

Access to Document

10.1145/2382336.2382370

Cite this

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title = "Neighborhood preserving ordinal regression",

abstract = "Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.",

keywords = "Manifold learning, Maximum margin, Neighborhood Preserving Ordinal Regression",

author = "Yang Liu and Yan Liiu and Chan, \{Keith C.C.\} and Jing Zhang",

note = "This work was supported by China Over-sea Cooperation Grants with State Key Laboratory for Novel Software Technology Fund Video Retargeting using Spatiotemporal Optimization. Publisher Copyright: Copyright 2012 ACM ; 4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012, ICIMCS '12 ; Conference date: 09-09-2012 Through 11-09-2012",

year = "2012",

month = sep,

doi = "10.1145/2382336.2382370",

language = "English",

isbn = "9781450316002",

series = "ACM International Conference Proceeding Series",

pages = "119--122",

booktitle = "ICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service",

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Liu, Y, Liiu, Y, Chan, KCC & Zhang, J 2012, Neighborhood preserving ordinal regression. in ICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service. ACM International Conference Proceeding Series, pp. 119-122, 4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012, Wuhan, China, 9/09/12. https://doi.org/10.1145/2382336.2382370

TY - GEN

T1 - Neighborhood preserving ordinal regression

AU - Liu, Yang

AU - Liiu, Yan

AU - Chan, Keith C.C.

AU - Zhang, Jing

N1 - This work was supported by China Over-sea Cooperation Grants with State Key Laboratory for Novel Software Technology Fund Video Retargeting using Spatiotemporal Optimization. Publisher Copyright: Copyright 2012 ACM

PY - 2012/9

Y1 - 2012/9

N2 - Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.

AB - Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.

KW - Manifold learning

KW - Maximum margin

KW - Neighborhood Preserving Ordinal Regression

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U2 - 10.1145/2382336.2382370

DO - 10.1145/2382336.2382370

M3 - Conference proceeding

AN - SCOPUS:84869045668

SN - 9781450316002

T3 - ACM International Conference Proceeding Series

SP - 119

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BT - ICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service

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ER -

Neighborhood preserving ordinal regression

Abstract

Publication series

Conference

UN SDGs

User-Defined Keywords

Access to Document

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