ORTHOGONAL MAXIMUM MARGIN DISCRIMINANT PROJECTION WITH APPLICATION TO LEAF IMAGE CLASSIFICATION

2014 ◽  
Vol 28 (04) ◽  
pp. 1450010 ◽  
Author(s):  
SHAN-WEN ZHANG ◽  
XIANFENG WANG ◽  
CHUANLEI ZHANG
Keyword(s):  
Image Data ◽  
Original Data ◽  
Dimensional Subspace ◽  
Neighborhood Density ◽  
Linear Discriminant ◽  
Plant Leaf ◽  
Maximum Margin ◽  
Data Points ◽  
Leaf Image ◽  
Discriminant Projection

A novel supervised dimensionality reduction method called orthogonal maximum margin discriminant projection (OMMDP) is proposed to cope with the high dimensionality, complex, various, irregular-shape plant leaf image data. OMMDP aims at learning a linear transformation. After projecting the original data into a low dimensional subspace by OMMDP, the data points of the same class get as near as possible while the data points of the different classes become as far as possible, thus the classification ability is enhanced. The main differences from linear discriminant analysis (LDA), discriminant locality preserving projections (DLPP) and other supervised manifold learning-based methods are as follows: (1) In OMMDP, Warshall algorithm is first applied to constructing both of the must-link and class-class scatter matrices, whose process is easily and quickly implemented without judging whether any pairwise points belong to the same class. (2) The neighborhood density is defined to construct the objective function of OMMDP, which makes OMMDP be robust to noise and outliers. Experimental results on two public plant leaf databases clearly demonstrate the effectiveness of the proposed method for classifying leaf images.

Semi-Supervised Discriminant Projection for Plant Leaf Classification

2013 ◽  
Vol 779-780 ◽  
pp. 1332-1335
Author(s):  
Shan Wen Zhang ◽  
Yi Jun Shang ◽  
Yun Long Zhang
Keyword(s):  
Dimension Reduction ◽  
Geometric Structure ◽  
Unlabeled Data ◽  
Reduction Algorithm ◽  
Plant Leaf ◽  
Neighborhood Information ◽  
Data Points ◽  
Discriminant Projection

Plant leaf classification is important but very difficult, because the leaf images are irregular and nonlinear. In this paper, we propose a novel semi-supervised method, called Semi-supervised discriminant projection (SSDP) dimension reduction algorithm for leaf recognition. SSDP makes full use of both labeled and unlabeled data to construct the weight incorporating the neighborhood information of data. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. The experiment results on a public plant leaf database demonstrate that SSDP is effective and feasible for plant leaf recognition.

scholarly journals Investigation of Polar Mesospheric Summer Echoes Using Linear Discriminant Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 522
Author(s):  
Dorota Jozwicki ◽  
Puneet Sharma ◽  
Ingrid Mann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Image Data ◽  
Atmospheric Conditions ◽  
Very High Frequency ◽  
Vhf Radar ◽  
Linear Discriminant ◽  
Radar Echoes ◽  
Summer Echoes ◽  
Percent Accuracy

Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

scholarly journals Unsupervised Text Feature Learning via Deep Variational Auto-encoder

2020 ◽  
Vol 49 (3) ◽  
pp. 421-437
Author(s):  
Genggeng Liu ◽  
Lin Xie ◽  
Chi-Hua Chen
Keyword(s):  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Image Data ◽  
Original Data ◽  
Feature Representation ◽  
High Dimensional ◽  
Learning To Learn ◽  
Text Feature ◽  
Reduction Methods ◽  
Low Dimensional

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

scholarly journals Discovering a sparse set of pairwise discriminating features in high-dimensional data

2020 ◽  
Author(s):  
Samuel Melton ◽  
Sharad Ramanathan
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Dimensional Subspace ◽  
Supplementary Information ◽  
High Dimensional ◽  
Technological Advances ◽  
Data Points ◽  
Low Dimensional ◽  
Sparse Set

Abstract Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Distributionally Adversarial Attack

2019 ◽  
Vol 33 ◽  
pp. 2253-2260 ◽  
Author(s):  
Tianhang Zheng ◽  
Changyou Chen ◽  
Kui Ren
Keyword(s):  
Data Distribution ◽  
Original Data ◽  
First Order ◽  
Risk Optimization ◽  
Wide Range ◽  
Data Points ◽  
Adversarial Attack ◽  
Projected Gradient Descent ◽  
Original Objective ◽  
Direct Dependency

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically in the form of risk maximization/minimization, e.g., max/min Ep(x) L(x) with p(x) some unknown data distribution and L(·) a loss function. However, since PGD generates attack samples independently for each data sample based on L(·), the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal adversarial-data distribution, a perturbed distribution that satisfies the L∞ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by MIT MadryLab. Notably, DAA ranks the first place on MadryLab’s white-box leaderboards, reducing the accuracy of their secret MNIST model to 88.56% (with l∞ perturbations of ε = 0.3) and the accuracy of their secret CIFAR model to 44.71% (with l∞ perturbations of ε = 8.0). Code for the experiments is released on https://github.com/tianzheng4/Distributionally-Adversarial-Attack.

SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

2006 ◽  
Vol 03 (01) ◽  
pp. 45-51
Author(s):  
YANWEI PANG ◽  
ZHENGKAI LIU ◽  
YUEFANG SUN
Keyword(s):  
Face Recognition ◽  
Subspace Learning ◽  
Dimensional Subspace ◽  
Training Data ◽  
Objective Functions ◽  
Recognition Method ◽  
Laplacian Eigenmaps ◽  
Linear Discriminant ◽  
Structure Information ◽  
Low Dimensional

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.

scholarly journals Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction

2019 ◽  
Vol 33 ◽  
pp. 3910-3918 ◽  
Author(s):  
Akira Imakura ◽  
Momo Matsuda ◽  
Xiucai Ye ◽  
Tetsuya Sakurai
Keyword(s):  
Dimensionality Reduction ◽  
Parallel Implementation ◽  
Dimensional Space ◽  
Recognition Performance ◽  
Optimization Methods ◽  
Original Data ◽  
Dimensional Subspace ◽  
Reduction Methods ◽  
Low Dimensional ◽  
Matrix Trace

Dimensionality reduction methods that project highdimensional data to a low-dimensional space by matrix trace optimization are widely used for clustering and classification. The matrix trace optimization problem leads to an eigenvalue problem for a low-dimensional subspace construction, preserving certain properties of the original data. However, most of the existing methods use only a few eigenvectors to construct the low-dimensional space, which may lead to a loss of useful information for achieving successful classification. Herein, to overcome the deficiency of the information loss, we propose a novel complex moment-based supervised eigenmap including multiple eigenvectors for dimensionality reduction. Furthermore, the proposed method provides a general formulation for matrix trace optimization methods to incorporate with ridge regression, which models the linear dependency between covariate variables and univariate labels. To reduce the computational complexity, we also propose an efficient and parallel implementation of the proposed method. Numerical experiments indicate that the proposed method is competitive compared with the existing dimensionality reduction methods for the recognition performance. Additionally, the proposed method exhibits high parallel efficiency.

Identification of Outliers in Hyperspectral Raman Image Data by Nearest Neighbor Comparison

2002 ◽  
Vol 56 (11) ◽  
pp. 1458-1461 ◽  
Author(s):  
Caleb J. Behrend ◽  
Catherine P. Tarnowski ◽  
Michael D. Morris
Keyword(s):  
Nearest Neighbor ◽  
Polynomial Interpolation ◽  
Image Data ◽  
Data Sets ◽  
Outlier Identification ◽  
Contaminated Data ◽  
Central Pixel ◽  
Data Points ◽  
Highly Correlated ◽  
Raman Image

A new algorithm for removal of cosmic spikes from hyperspectral Raman image data sets is presented. Spectra in a 3 × 3 pixel neighborhood are used to identify outlier-contaminated data points in the central pixel of that neighborhood. A preliminary despiking of the neighboring spectra is performed by median filtering. Correlations between the central pixel spectrum and its despiked neighbors are calculated, and the most highly correlated spectrum is used to identify outliers. Spike-contaminated data are replaced using results of polynomial interpolation. Because the neighborhood contains spectra obtained in three different frames, even large multi-pixel spikes are identified. Spatial, spectral, and temporal variation in signal is used to accurately identify outliers without the acquisition of any spectra other than those needed to generate the image itself. Sharp boundaries between regions of high chemical contrast do not interfere with outlier identification.

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