An improved study of locality sensitive discriminant analysis for object recognition

2015 ◽  
Author(s):  
Liu Liu ◽  
Fuqiang Zhou ◽  
Yuzhu He
Keyword(s):  
Object Recognition ◽  
Discriminant Analysis ◽  
Locality Sensitive Discriminant Analysis

scholarly journals Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Huang Yao ◽  
Yu Zhang ◽  
Yantao Wei ◽  
Yuan Tian
Keyword(s):  
Discriminant Analysis ◽  
Manifold Learning ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Learning System ◽  
Hyperspectral Image Classification ◽  
Successful Model ◽  
Learning Tasks ◽  
Input Layer ◽  
Locality Sensitive Discriminant Analysis

In this paper, we propose a new method for hyperspectral images (HSI) classification, aiming to take advantage of both manifold learning-based feature extraction and neural networks by stacking layers applying locality sensitive discriminant analysis (LSDA) to broad learning system (BLS). BLS has been proven to be a successful model for various machine learning tasks due to its high feature representative capacity introduced by numerous randomly mapped features. However, it also produces redundancy, which is indiscriminate and finally lowers its performance and causes heavy computing demand, especially in cases of the input data bearing high dimensionality. In our work, a manifold learning method is integrated into the BLS by inserting two LSDA layers before the input layer and output layer separate, so the spectral-spatial HSI features are fully utilized to acquire the state-of-the-art classification accuracy. The extensive experiments have shown our method’s superiority.

TENSOR LOCALITY SENSITIVE DISCRIMINANT ANALYSIS AND ITS COMPLEXITY

2009 ◽  
Vol 07 (06) ◽  
pp. 865-880 ◽  
Author(s):  
YANTAO WEI ◽  
HONG LI ◽  
LUOQING LI
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Time Complexity ◽  
High Performance ◽  
High Accuracy ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Locality Sensitive Discriminant Analysis

Feature extraction is one of the most challenging problems in pattern recognition fields and has attracted great attention recently. In this paper, we propose a novel feature extraction algorithm named tensor locality sensitive discriminant analysis which accepts tensors as inputs. The algorithm preserves the key structure of data by using the labeled samples and has high performance as well as low time complexity. Experiments on the three standard databases show that the proposed method has better performance and achieves high accuracy.

Stable locality sensitive discriminant analysis for image recognition

2014 ◽  
Vol 54 ◽  
pp. 49-56 ◽  
Author(s):  
Quanxue Gao ◽  
Jingjing Liu ◽  
Kai Cui ◽  
Hailin Zhang ◽  
Xiaogang Wang
Keyword(s):  
Discriminant Analysis ◽  
Image Recognition ◽  
Locality Sensitive Discriminant Analysis
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