Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data

Yi Guo; Junbin Gao; Paul H Kwan

doi:10.1007/978-3-642-10439-8_25

Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data

Yi Guo
, Junbin Gao
, Paul H Kwan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.

Original language	English
Title of host publication	AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings
Editors	Ann Nicholson, Xiaodong Li
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	240-249
ISBN (Print)	9783642104381, 364210438X
DOIs	https://doi.org/10.1007/978-3-642-10439-8_25
Publication status	Published - 2009
Event	AI 2009: 22nd Australasian Joint Conference Melbourne - Melbourne, Australia Duration: 1 Dec 2009 → 4 Dec 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	5866
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	AI 2009: 22nd Australasian Joint Conference Melbourne
City	Melbourne, Australia
Period	1/12/09 → 4/12/09

Keywords

Pattern Recognition and Data Mining
Neural, Evolutionary and Fuzzy Computation

Access to Document

10.1007/978-3-642-10439-8_25

https://hdl.handle.net/1959.11/5598

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Guo, Y., Gao, J., & Kwan, P. H. (2009). Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. In A. Nicholson, & X. Li (Eds.), AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings (pp. 240-249). (Lecture Notes in Computer Science; No. 5866). Springer. https://doi.org/10.1007/978-3-642-10439-8_25

Guo, Yi ; Gao, Junbin ; Kwan, Paul H. / Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings. editor / Ann Nicholson ; Xiaodong Li. Berlin, Germany : Springer, 2009. pp. 240-249 (Lecture Notes in Computer Science; 5866).

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title = "Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data",

abstract = "In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.",

keywords = "Pattern Recognition and Data Mining, Neural, Evolutionary and Fuzzy Computation",

author = "Yi Guo and Junbin Gao and Kwan, \{Paul H\}",

year = "2009",

doi = "10.1007/978-3-642-10439-8\_25",

language = "English",

isbn = "9783642104381",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

number = "5866",

pages = "240--249",

editor = "Ann Nicholson and Xiaodong Li",

booktitle = "AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings",

note = "AI 2009: 22nd Australasian Joint Conference Melbourne ; Conference date: 01-12-2009 Through 04-12-2009",

}

Guo, Y, Gao, J & Kwan, PH 2009, Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. in A Nicholson & X Li (eds), AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings. Lecture Notes in Computer Science, no. 5866, Springer, Berlin, Germany, pp. 240-249, AI 2009: 22nd Australasian Joint Conference Melbourne, Melbourne, Australia, 1/12/09. https://doi.org/10.1007/978-3-642-10439-8_25

Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. / Guo, Yi; Gao, Junbin; Kwan, Paul H.
AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings. ed. / Ann Nicholson; Xiaodong Li. Berlin, Germany: Springer, 2009. p. 240-249 (Lecture Notes in Computer Science; No. 5866).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data

AU - Guo, Yi

AU - Gao, Junbin

AU - Kwan, Paul H

PY - 2009

Y1 - 2009

N2 - In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.

AB - In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.

KW - Pattern Recognition and Data Mining

KW - Neural, Evolutionary and Fuzzy Computation

U2 - 10.1007/978-3-642-10439-8_25

DO - 10.1007/978-3-642-10439-8_25

M3 - Conference contribution

SN - 9783642104381

SN - 364210438X

T3 - Lecture Notes in Computer Science

SP - 240

EP - 249

BT - AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings

A2 - Nicholson, Ann

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PB - Springer

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T2 - AI 2009: 22nd Australasian Joint Conference Melbourne

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ER -

Guo Y, Gao J, Kwan PH. Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data. In Nicholson A, Li X, editors, AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings. Berlin, Germany: Springer. 2009. p. 240-249. (Lecture Notes in Computer Science; 5866). doi: 10.1007/978-3-642-10439-8_25

Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data

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