Computational intelligence for structure preserving dimensionality reduction and feature analysis

2005 ◽  
Author(s):  
N.R. Pal
Keyword(s):  
Dimensionality Reduction ◽  
Computational Intelligence ◽  
Feature Analysis ◽  
Structure Preserving

Structure preserving dimensionality reduction for visual object recognition

2018 ◽  
Vol 77 (18) ◽  
pp. 23529-23545 ◽  
Author(s):  
Jinjoo Song ◽  
Gangjoon Yoon ◽  
Heeryon Cho ◽  
Sang Min Yoon
Keyword(s):  
Object Recognition ◽  
Dimensionality Reduction ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Structure Preserving

scholarly journals Structure-Preserving Denoising of SAR Images Using Multifractal Feature Analysis

2020 ◽  
Vol 17 (12) ◽  
pp. 2100-2104 ◽  
Author(s):  
Suman Kumar Maji ◽  
Ramesh Kumar Thakur ◽  
Hussein M. Yahia
Keyword(s):  
Feature Analysis ◽  
Sar Images ◽  
Structure Preserving

scholarly journals Line-segment Feature Analysis Algorithm Using Input Dimensionality Reduction for Handwritten Text Recognition

2020 ◽  
Vol 10 (19) ◽  
pp. 6904
Author(s):  
Chang-Min Kim ◽  
Ellen J. Hong ◽  
Kyungyong Chung ◽  
Roy C. Park
Keyword(s):  
Dimensionality Reduction ◽  
Line Segment ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Feature Analysis ◽  
Support Vector ◽  
License Plate ◽  
Field Methods ◽  
Handwritten Text ◽  
Handwritten Text Recognition

Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have been applied to handwriting recognition. However, owing to the diversity of recognition application fields, the number of dimensions in the learning and reasoning processes is increasing. To solve this problem, a principal component analysis (PCA) technique is used for dimensionality reduction. However, PCA is likely to increase the accuracy loss due to data compression. Therefore, in this paper, we propose a line-segment feature analysis (LFA) algorithm for input dimensionality reduction in handwritten text recognition. This proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using 3 × 3 and 5 × 5 filters. Using the unique values to identify the number of line segments and adding them up, a 1-D vector with a size of 512 is created. This vector is used as input to machine-learning. For the performance evaluation of the method, the Extending Modified National Institute of Standards and Technology (EMNIST) database was used. In the evaluation, PCA showed 96.6% and 93.86% accuracy with k-nearest neighbors (KNN) and support vector machine (SVM), respectively, while LFA showed 97.5% and 98.9% accuracy with KNN and SVM, respectively.

scholarly journals Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction

2021 ◽  
Vol 13 (14) ◽  
pp. 2752
Author(s):  
Na Li ◽  
Deyun Zhou ◽  
Jiao Shi ◽  
Tao Wu ◽  
Maoguo Gong
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Cluster Centroid ◽  
Intrinsic Structure ◽  
Adjacency Graph ◽  
Structure Preserving ◽  
Class Labels

Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.

scholarly journals Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Bingfeng Li ◽  
Yandong Tang ◽  
Zhi Han
Keyword(s):  
Dimensionality Reduction ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Noisy Data ◽  
Nonnegative Matrix ◽  
Structure Preserving ◽  
Structure Preservation ◽  
Significant Performance ◽  
Linear Dimensionality Reduction ◽  
Low Dimensional

As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining. However, there are still two major drawbacks for NMF: (a) NMF can only perform semantic factorization in Euclidean space, and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution. (b) NMF suffers from noisy data, which are commonly encountered in real-world applications. To address these issues, in this paper, we present a new robust structure preserving nonnegative matrix factorization (RSPNMF) framework. In RSPNMF, a local affinity graph and a distant repulsion graph are constructed to encode the geometrical information, and noisy data influence is alleviated by characterizing the data reconstruction term of NMF withl2,1-norm instead ofl2-norm. With incorporation of the local and distant structure preservation regularization term into the robust NMF framework, our algorithm can discover a low-dimensional embedding subspace with the nature of structure preservation. RSPNMF is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm. Experimental results on some facial image datasets clustering show significant performance improvement of RSPNMF in comparison with the state-of-the-art algorithms.

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