Improvement of the kernel minimum squared error model for fast feature extraction

2012 ◽  
Vol 23 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Jinghua Wang ◽  
Peng Wang ◽  
Qin Li ◽  
Jane You
Keyword(s):  
Feature Extraction ◽  
Error Model ◽  
Squared Error ◽  
Kernel Minimum Squared Error

Feature Extraction for Kernel Minimum Squared Error by Sparsity Shrinkage

2014 ◽  
Vol 536-537 ◽  
pp. 450-453 ◽  
Author(s):  
Jiang Jiang ◽  
Xi Chen ◽  
Hai Tao Gan
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Optimization Problem ◽  
Subset Selection ◽  
Experimental Results ◽  
Computational Burden ◽  
Squared Error ◽  
Kernel Minimum Squared Error ◽  
Benchmark Datasets ◽  
Training Examples

In this paper, a sparsity based model is proposed for feature selection in kernel minimum squared error (KMSE). By imposing a sparsity shrinkage term, we formulate the procedure of subset selection as an optimization problem. With the chosen small portion of training examples, the computational burden of feature extraction is largely alleviated. Experimental results conducted on several benchmark datasets indicate the effectivity and efficiency of our method.

Discriminant Criteria for Pattern Classification

2011 ◽  
pp. 30-57
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
The Other ◽  
Linear Projection ◽  
Linear Discriminant ◽  
Large Margin ◽  
The Third ◽  
Fisher Discriminant ◽  
Squared Error ◽  
Fisher Discriminant Criterion

As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book, we focus our attention on the feature extraction aspect of LDA for SSS problems. On the other hand,, with this chapter we present our studies on the pattern classification aspect of LDA for SSS problems. In this chapter, we present three novel classification-oriented linear discriminant criteria. The first one is large margin linear projection (LMLP) which makes full use of the characteristic of the SSS problems. The second one is the minimum norm minimum squared-error criterion which is a modification of the minimum squared-error discriminant criterion. The third one is the maximum scatter difference which is a modification of the Fisher discriminant criterion.

Towards a probabilistic semi-supervised Kernel Minimum Squared Error algorithm

2016 ◽  
Vol 171 ◽  
pp. 149-155 ◽  
Author(s):  
Haitao Gan ◽  
Rui Huang ◽  
Zhizeng Luo ◽  
Yingle Fan ◽  
Farong Gao
Keyword(s):  
Squared Error ◽  
Kernel Minimum Squared Error

Giving Order to Districts: Estimating Voter Distributions with National Election Returns

2009 ◽  
Vol 17 (3) ◽  
pp. 215-235 ◽  
Author(s):  
Georgia Kernell
Keyword(s):  
Empirical Research ◽  
Bayesian Model ◽  
Voting Behavior ◽  
Strategic Behavior ◽  
Error Model ◽  
Congressional District ◽  
Squared Error ◽  
Out Of Sample ◽  
The Common ◽  
Direct Measures

Correctly measuring district preferences is crucial for empirical research on legislative responsiveness and voting behavior. This article argues that the common practice of using presidential vote shares to measure congressional district ideology systematically produces incorrect estimates. I propose an alternative method that employs multiple election returns to estimate voters' ideological distributions within districts. I develop two estimation procedures—a least squared error model and a Bayesian model—and test each with simulations and empirical applications. The models are shown to outperform vote shares, and they are validated with direct measures of voter ideology and out-of-sample election predictions. Beyond estimating district ideology, these models provide valuable information on constituency heterogeneity—an important, but often immeasurable, quantity for research on representatives— strategic behavior.

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