scholarly journals Stochastic discriminant analysis for linear supervised dimension reduction

2018 ◽  
Vol 291 ◽  
pp. 136-150 ◽  
Author(s):  
Mika Juuti ◽  
Francesco Corona ◽  
Juha Karhunen
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Supervised Dimension Reduction

Scaling cut criterion-based discriminant analysis for supervised dimension reduction

2014 ◽  
Vol 43 (3) ◽  
pp. 633-655 ◽  
Author(s):  
Xiangrong Zhang ◽  
Yudi He ◽  
Licheng Jiao ◽  
Ruochen Liu ◽  
Jie Feng ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Supervised Dimension Reduction

Dimension Reduction of Multimodal Data by Auto-weighted Local Discriminant Analysis

2021 ◽  
Author(s):  
Rongxiu Lu ◽  
Yingjie Cai ◽  
Jianyong Zhu ◽  
Feiping Nie ◽  
Hui Yang
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Multimodal Data

scholarly journals QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization

2015 ◽  
Vol 43 (4) ◽  
pp. 1498-1534 ◽  
Author(s):  
Jianqing Fan ◽  
Zheng Tracy Ke ◽  
Han Liu ◽  
Lucy Xia
Keyword(s):  
Dimension Reduction ◽  
Reduction Method ◽  
Rayleigh Quotient ◽  
Dimension Reduction Method ◽  
Supervised Dimension Reduction

Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

2017 ◽  
Vol 39 (6) ◽  
pp. 1696-1712 ◽  
Author(s):  
Weibao Du ◽  
Wenwen Qiang ◽  
Meng Lv ◽  
Qiuling Hou ◽  
Ling Zhen ◽  
...  
Keyword(s):  
Dimension Reduction ◽  
Hyperspectral Images ◽  
Supervised Dimension Reduction

scholarly journals Dimension Reduction for Objects Composed of Vector Sets

2017 ◽  
Vol 27 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Marton Szemenyei ◽  
Ferenc Vajda
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Dimension Reduction ◽  
Pose Estimation ◽  
Real World ◽  
Single Object ◽  
Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

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