scholarly journals Large Margin Graph Embedding-Based Discriminant Dimensionality Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanjia Tian ◽  
Xiang Feng
Keyword(s):  
Dimensionality Reduction ◽  
Evaluation Method ◽  
Graph Embedding ◽  
Classification Performance ◽  
Intrinsic Geometry ◽  
The Past ◽  
Discriminant Ability ◽  
Reduction Methods ◽  
Public Datasets ◽  
Better Than

Discriminant graph embedding-based dimensionality reduction methods have attracted more and more attention over the past few decades. These methods construct an intrinsic graph and penalty graph to preserve the intrinsic geometry structures of intraclass samples and separate the interclass samples. However, the marginal samples cannot be accurately characterized only by penalty graphs since they treat every sample equally. In practice, these marginal samples often influence the classification performance, which needs to be specially tackled. In this study, the near neighbors’ hypothesis margin of marginal samples has been further maximized to separate the interclass samples and improve the discriminant ability by integrating intrinsic graph and penalty graph. A novel discriminant dimensionality reduction named LMGE-DDR has been proposed. Several experiments on public datasets have been conducted to verify the effectiveness of the proposed LMGE-DDR such as ORL, Yale, UMIST, FERET, CMIU-PIE09, and AR. LMGE-DDR performs better than other compared methods, and the corresponding standard deviation of LMGE-DDR is smaller than others. This demonstrates that the evaluation method verifies the effectiveness of the introduced method.

scholarly journals Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

2019 ◽  
Vol 11 (9) ◽  
pp. 1039 ◽  
Author(s):  
Hong Huang ◽  
Meili Chen ◽  
Yule Duan
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Spatial Coherence ◽  
Graph Embedding ◽  
Classification Performance ◽  
Spectral Structure ◽  
Coherence Property ◽  
Dr Method ◽  
Point To Point ◽  
Embedding Methods

Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

scholarly journals Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Jianwei Zheng ◽  
Hangke Zhang ◽  
Carlo Cattani ◽  
Wanliang Wang
Keyword(s):  
Dimensionality Reduction ◽  
System Analysis ◽  
Classification Performance ◽  
Living System ◽  
Linear Projection ◽  
Reduction Methods ◽  
Gradient Based ◽  
Class Labels ◽  
Global And Local ◽  
Training Efficiency

Dimensionality reduction is an important issue for numerous applications including biomedical images analysis and living system analysis. Neighbor embedding, those representing the global and local structure as well as dealing with multiple manifolds, such as the elastic embedding techniques, can go beyond traditional dimensionality reduction methods and find better optima. Nevertheless, existing neighbor embedding algorithms can not be directly applied in classification as suffering from several problems: (1) high computational complexity, (2) nonparametric mappings, and (3) lack of class labels information. We propose a supervised neighbor embedding called discriminative elastic embedding (DEE) which integrates linear projection matrix and class labels into the final objective function. In addition, we present the Laplacian search direction for fast convergence. DEE is evaluated in three aspects: embedding visualization, training efficiency, and classification performance. Experimental results on several benchmark databases present that the proposed DEE exhibits a supervised dimensionality reduction approach which not only has strong pattern revealing capability, but also brings computational advantages over standard gradient based methods.

scholarly journals Sub-Graph Regularization on Kernel Regression for Robust Semi-Supervised Dimensionality Reduction

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1125
Author(s):  
Jiao Liu ◽  
Mingbo Zhao ◽  
Weijian Kong
Keyword(s):  
Dimensionality Reduction ◽  
Supervised Learning ◽  
Kernel Regression ◽  
Classification Performance ◽  
Unlabeled Data ◽  
Graph Regularization ◽  
Linear Discriminant ◽  
Benchmark Datasets ◽  
Reduction Methods ◽  
Class Labels

Dimensionality reduction has always been a major problem for handling huge dimensionality datasets. Due to the utilization of labeled data, supervised dimensionality reduction methods such as Linear Discriminant Analysis tend achieve better classification performance compared with unsupervised methods. However, supervised methods need sufficient labeled data in order to achieve satisfying results. Therefore, semi-supervised learning (SSL) methods can be a practical selection rather than utilizing labeled data. In this paper, we develop a novel SSL method by extending anchor graph regularization (AGR) for dimensionality reduction. In detail, the AGR is an accelerating semi-supervised learning method to propagate the class labels to unlabeled data. However, it cannot handle new incoming samples. We thereby improve AGR by adding kernel regression on the basic objective function of AGR. Therefore, the proposed method can not only estimate the class labels of unlabeled data but also achieve dimensionality reduction. Extensive simulations on several benchmark datasets are conducted, and the simulation results verify the effectiveness for the proposed work.

scholarly journals Dimensionality Reduction Algorithms on High Dimensional Datasets

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Iwan Syarif
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Particle Swarm ◽  
Classification Problem ◽  
Classification Performance ◽  
High Dimensional ◽  
Selection Algorithms ◽  
High Dimensional Datasets ◽  
Better Than

Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GA‐ reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO).

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Implementasi Algoritma Hamming Distance dan Brute Force dalam Mendeteksi Kemiripan Source Code Bahasa Pemrograman C

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Andreas Budiman ◽  
Dennis Gunawan ◽  
Seng Hansun
Keyword(s):  
College Presidents ◽  
Hamming Distance ◽  
Source Code ◽  
Structural Type ◽  
Brute Force ◽  
Important Thing ◽  
The Past ◽  
Index Terms ◽  
Better Than

Plagiarism is a behavior that causes violence of copyrights. Survey shows 55% of college presidents say that plagiarism in students’ papers has increased over the past 10 years. Therefore, an application for detecting plagiarism is needed, especially for teachers. This plagiarism checker application is made by using Visual C# 2010. The plagiarism checker uses hamming distance algorithm for matching line code of the source code. This algorithm works by matching the same length string of the code programs. Thus, it needs brute will be matched with hamming distance. Another important thing for detecting plagiarism is the preprocessing, which is used to help the algorithm for detecting plagiarized source code. This paper shows that the application works good in detecting plagiarism, the hamming distance algorithm and brute force algorithm works better than levenstein distance algorithm for detecting structural type of plagiarism and this thesis also shows that the preprocessing could help the application to increase its percentage and its accuracy. Index Terms—Brute Force, Hamming Distance, Plagiarisme, Preprocessing.

Ways of Being and Time

2017 ◽  
Author(s):  
Kris McDaniel
Keyword(s):  
Being And Time ◽  
Present Moment ◽  
Ontological Pluralism ◽  
The Past ◽  
Mode Of Being ◽  
Ways Of Being ◽  
Better Than

This chapter develops a version of ontological pluralism that respects two common intuitions about time: that the present moment is metaphysically distinguished but not in such a way that the past is unreal. The version of ontological pluralism developed—presentist existential pluralism (PEP)—embraces two modes of being, the mode of being that present objects enjoy and the mode of being that past objects enjoy. The author argues that this view fares at least as well, and probably better, than other views in which the present is metaphysically distinguished. The chapter also introduces another form of ontological superiority called “levels of being.”

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

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