A Scalable Two-Stage Approach for Dimensionality Reduction

The importance of replication in gene-gene interaction studies: multifactor dimensionality reduction applied to a two-stage breast cancer case-control study

Carcinogenesis ◽

10.1093/carcin/bgn120 ◽

2008 ◽

Vol 29 (6) ◽

pp. 1215-1218 ◽

Cited By ~ 16

Author(s):

R. L. Milne ◽

R. Fagerholm ◽

H. Nevanlinna ◽

J. Benitez

Keyword(s):

Breast Cancer ◽

Dimensionality Reduction ◽

Multifactor Dimensionality Reduction ◽

Case Control Study ◽

Breast Cancer Case ◽

Gene Interaction ◽

Case Control ◽

Cancer Case ◽

Two Stage ◽

Control Study

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Two-stage dimensionality reduction approach based on 2DLDA and fuzzy rough sets technique

Neurocomputing ◽

10.1016/j.neucom.2011.06.020 ◽

2011 ◽

Vol 74 (17) ◽

pp. 3722-3727 ◽

Cited By ~ 3

Author(s):

Hao-Xin Zhao ◽

Hong-Jie Xing ◽

Xi-Zhao Wang

Keyword(s):

Dimensionality Reduction ◽

Rough Sets ◽

Fuzzy Rough Sets ◽

Two Stage ◽

Reduction Approach

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Design-Oriented Two-Stage Surrogate Modeling of Miniaturized Microstrip Circuits With Dimensionality Reduction

IEEE Access ◽

10.1109/access.2020.3006708 ◽

2020 ◽

Vol 8 ◽

pp. 121744-121754 ◽

Cited By ~ 3

Author(s):

Slawomir Koziel ◽

Anna Pietrenko-Dabrowska ◽

Mu'ath Al-Hasan

Keyword(s):

Dimensionality Reduction ◽

Surrogate Modeling ◽

Two Stage

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A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Journal of Applied Mathematics ◽

10.1155/2014/654787 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Mohammad Amin Shayegan ◽

Saeed Aghabozorgi ◽

Ram Gopal Raj

Keyword(s):

Principal Component Analysis ◽

Pattern Recognition ◽

Feature Selection ◽

Dimensionality Reduction ◽

Feature Vector ◽

Principal Component ◽

Random Projection ◽

Component Analysis ◽

Two Stage ◽

Linear Discriminant

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers.

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A scalable two-stage approach for a class of dimensionality reduction techniques

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10 ◽

10.1145/1835804.1835846 ◽

2010 ◽

Cited By ~ 15

Author(s):

Liang Sun ◽

Betul Ceran ◽

Jieping Ye

Keyword(s):

Dimensionality Reduction ◽

Two Stage ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques

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Two-stage multiple kernel learning for supervised dimensionality reduction

Pattern Recognition ◽

10.1016/j.patcog.2014.12.001 ◽

2015 ◽

Vol 48 (5) ◽

pp. 1854-1862 ◽

Cited By ~ 36

Author(s):

Abdollah Nazarpour ◽

Peyman Adibi

Keyword(s):

Dimensionality Reduction ◽

Multiple Kernel Learning ◽

Kernel Learning ◽

Two Stage ◽

Multiple Kernel

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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