Incremental Projection Vector Machine: A One-Stage Learning Algorithm for High-Dimension Large-Sample Dataset

2010 ◽  
pp. 132-141 ◽  
Author(s):  
Qinghua Zheng ◽  
Xin Wang ◽  
Wanyu Deng ◽  
Jun Liu ◽  
Xiyuan Wu
Keyword(s):  
High Dimension ◽  
Learning Algorithm ◽  
Large Sample ◽  
Projection Vector ◽  
One Stage

Performance Analysis of Machine Learning Algorithm for Healthcare Tools with High Dimension Segmentation

2020 ◽  
pp. 115-128
Author(s):  
Soobia Saeed ◽  
Afnizanfaizal Abdullah ◽  
N. Z. Jhanjhi ◽  
Mehmood Naqvi ◽  
Mamoona Humayun
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
High Dimension ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

New Measure Based Manifold Algorithm and Application in Anomaly Detection of Hyperspectral Imagery

2011 ◽  
Vol 80-81 ◽  
pp. 797-803
Author(s):  
Liang Liang Wang ◽  
Zhi Yong Li ◽  
Ji Xiang Sun ◽  
Chun Du
Keyword(s):  
Anomaly Detection ◽  
Manifold Learning ◽  
High Dimension ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Detection Performance ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Nonlinear Structure ◽  
Spectral Angle

Hyperspectral data is endowed with characteristics of intrinsic nonlinear structure and high dimension. In this paper, a nonlinear manifold learning algorithm - ISOMAP is applied to anomaly detection. Then an improved ISOMAP algorithm is developed based on the analysis of the inherent characteristics of hyperspectral imagery. The improved ISOMAP algorithm selects neighborhood according to a novel measure of combination of spectral gradient and spectral angle in order to make the algorithm more robust to the changes of light and terrain. Experimental results prove the effectiveness of the algorithm in improving the detection performance.

scholarly journals Online Sequential Projection Vector Machine with Adaptive Data Mean Update

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Lin Chen ◽  
Ji-Ting Jia ◽  
Qiong Zhang ◽  
Wan-Yu Deng ◽  
Wei Wei
Keyword(s):  
Dimension Reduction ◽  
Learning Algorithm ◽  
Performance Comparison ◽  
Stochastic Gradient Descent ◽  
High Dimensional ◽  
Model Parameters ◽  
Classification Problems ◽  
Projection Vector ◽  
Dimensional Classification ◽  
Hidden Nodes

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including(1)the projection vectors for dimension reduction,(2)the input weights, biases, and output weights, and(3)the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

scholarly journals I'm Simply the Best, Better than All the Rest - Narcissistic Leaders and Corporate Fundraising Success

2020 ◽  
Author(s):  
Dritjon Gruda ◽  
Jim McCleskey ◽  
Dimitra Karanatsiou ◽  
Athena Vakali
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Organizational Leaders ◽  
Grandiose Narcissism ◽  
Large Sample ◽  
The Relationship ◽  
Better Than

We examine the relationship between leader grandiose narcissism, composed of admiration and rivalry, and corporate fundraising success in a sample of 2377 organizational leaders. To examine a large sample of leaders, we applied a machine-learning algorithm to predict leaders' personality scores based on leaders' Twitter profiles. We found that admiration was positively related to - while rivalry was negatively related to corporate fundraising success (in '000s). Analyses also showed that leader gender does not moderate this relationship, unlike initially expected. We discuss and compare our findings to previous work on narcissism and crowdfunding.

Double stapled transabdominal anastomosis for one-stage resection of acute sigmoid volvulus

1997 ◽  
Vol 84 (1) ◽  
pp. 84-85
Author(s):  
R. FAROUK ◽  
M. ROGERS ◽  
P. W. R. LEE
Keyword(s):  
Sigmoid Volvulus ◽  
One Stage
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