A BOOSTED MANIFOLD LEARNING FOR AUTOMATIC FACE RECOGNITION

2010 ◽  
Vol 24 (02) ◽  
pp. 321-335 ◽  
Author(s):  
CHUNYUAN LU ◽  
JIANMIN JIANG ◽  
GUOCAN FENG
Keyword(s):  
Face Recognition ◽  
Manifold Learning ◽  
Reduction Method ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Nonlinear Structures ◽  
Effective Dimension ◽  
Dimension Reduction Method ◽  
Effective Dimension Reduction ◽  
Analysis Computer

Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates.

scholarly journals Dimension Reduction Analysis of Vowel Signal Data Based on Manifold Learning

2021 ◽  
Vol 18 ◽  
pp. 148-151
Author(s):  
Jinqing Shen ◽  
Zhongxiao Li ◽  
Xiaodong Zhuang
Keyword(s):  
Dimension Reduction ◽  
Manifold Learning ◽  
Speech Signal ◽  
Reduction Method ◽  
High Dimensional ◽  
Speech Signal Processing ◽  
Dimension Reduction Method ◽  
Important Method ◽  
Signal Features ◽  
Reduction Methods

Data dimension reduction is an important method to overcome dimension disaster and obtain as much valuable information as possible. Speech signal is a kind of non-stationary random signal with high redundancy, and proper dimension reduction methods are needed to extract and analyze the signal features efficiently in speech signal processing. Studies have shown that manifold structure exists in high-dimensional data. Manifold dimension reduction method aiming at discovering the intrinsic geometric structure of data may be more effective in dealing with practical problems. This paper studies a data dimension reduction method based on manifold learning and applies it to the analysis of vowel signals.

Failure Mode Recognition Clustering Algorithm Based on Manifold Learning

2012 ◽  
Vol 263-266 ◽  
pp. 2126-2130 ◽  
Author(s):  
Zhi Gang Lou ◽  
Hong Zhao Liu
Keyword(s):  
Manifold Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Spectral Characteristics ◽  
Normal Operation ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Pattern Clustering ◽  
Oil Pipeline ◽  
Adjustable Valve

Manifold learning is a new unsupervised learning method. Its main purpose is to find the inherent law of generated data sets. Be used for high dimensional nonlinear fault samples for learning, in order to identify embedded in high dimensional data space in the low dimensional manifold, can be effective data found the essential characteristics of fault identification. In many types of fault, sometimes often failure and normal operation of the equipment of some operation similar to misjudgment, such as oil pipeline transportation process, pipeline regulating pump, adjustable valve, pump switch, normal operation and pipeline leakage fault condition similar spectral characteristics, thus easy for pipeline leakage cause mistakes. This paper uses the manifold learning algorithm for fault pattern clustering recognition, and through experiments on the algorithm is evaluated.

FSLLE: A Fast K Selection Algorithm for Locally Linear Embedding

2018 ◽  
Vol 17 (01) ◽  
pp. 1850003
Author(s):  
Jin-Hang Liu ◽  
Tao Peng ◽  
Xiaogang Zhao ◽  
Kunfang Song ◽  
Minghua Jiang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Correlation ◽  
Manifold Learning ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Dimensional Manifold ◽  
Correlation Index ◽  
Recognition Algorithms ◽  
Linear Embedding ◽  
Locally Linear

Data in a high-dimensional data space may reside in a low-dimensional manifold embedded within the high-dimensional space. Manifold learning discovers intrinsic manifold data structures to facilitate dimensionality reductions. We propose a novel manifold learning technique called fast [Formula: see text] selection for locally linear embedding or FSLLE, which judiciously chooses an appropriate number (i.e., parameter [Formula: see text]) of neighboring points where the local geometric properties are maintained by the locally linear embedding (LLE) criterion. To measure the spatial distribution of a group of neighboring points, FSLLE relies on relative variance and mean difference to form a spatial correlation index characterizing the neighbors’ data distribution. The goal of FSLLE is to quickly identify the optimal value of parameter [Formula: see text], which aims at minimizing the spatial correlation index. FSLLE optimizes parameter [Formula: see text] by making use of the spatial correlation index to discover intrinsic structures of a data point’s neighbors. After implementing FSLLE, we conduct extensive experiments to validate the correctness and evaluate the performance of FSLLE. Our experimental results show that FSLLE outperforms the existing solutions (i.e., LLE and ISOMAP) in manifold learning and dimension reduction. We apply FSLLE to face recognition in which FSLLE achieves higher accuracy than the state-of-the-art face recognition algorithms. FSLLE is superior to the face recognition algorithms, because FSLLE makes a good tradeoff between classification precision and performance.

Hybrid multiplicative dimension reduction method for uncertainty analysis of engineering structures

2020 ◽  
pp. 1748006X2092997
Author(s):  
Haihe Li ◽  
Pan Wang ◽  
Qi Chang ◽  
Changcong Zhou ◽  
Zhufeng Yue
Keyword(s):  
Uncertainty Analysis ◽  
Dimension Reduction ◽  
Reduction Method ◽  
High Dimensional ◽  
Sensitivity Index ◽  
Dimension Reduction Method ◽  
Engineering Structures ◽  
High Order Derivative ◽  
Engineering Models ◽  
Complex Engineering

For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficiently smooth and has a small high-order derivative as the product of a series of one-dimensional functions, and then uses this approximation to calculate the statistical moments of the function. Then the variance-based global sensitivity index is employed to identify the important variables, and the identified important variables are subjected to bivariate decomposition approximation. Combined with the univariate multiplicative dimension reduction method, the hybrid decomposition approximation is obtained. Compared with the existing method, the proposed method is more accurate than the univariate decomposition approximation when used for uncertainty analysis of engineering models and needs less computational efforts than the bivariate decomposition. In the end, a numerical example and two engineering applications are tested to verify the effectiveness of the proposed method.

Optimal Classification of Hypersonic Inlet Start/Unstart Based on Manifold Learning

2013 ◽  
Vol 274 ◽  
pp. 200-203
Author(s):  
Ri Sheng Zheng ◽  
Jun Tao Chang ◽  
Hui Xin He ◽  
Fu Chen
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Operation Mode ◽  
High Dimensional ◽  
Classification Criterion ◽  
Decision Tree Algorithm ◽  
Time Data ◽  
Hypersonic Inlet ◽  
Optimal Classification

Inlet start/unstart detection has been the focus of researching hypersonic inlet, the operation mode of the inlet detection is the prerequisite for the unstart protection control of scramjet. Actually, due to computational complexity and high dimension discrete experimental data, all of these factors are against for the classification of real-time data. To solve this problem, firstly, the 2-D wind tunnel experiment is carried out, inlet start/unstart experiment phenomenon are analyzed; Secondly, isomap algorithm is introduced to reduce high dimensional data , the optimal classification method were obtained with the weighted embedded manifold learning algorithm, At last the superiority of the classification criterion is verified by decision tree algorithm.

Natural Nearest Neighbor for Isomap Algorithm without Free-Parameter

2011 ◽  
Vol 219-220 ◽  
pp. 994-998 ◽  
Author(s):  
Xian Lin Zou ◽  
Qing Sheng Zhu ◽  
Rui Long Yang
Keyword(s):  
Manifold Learning ◽  
Free Parameter ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Simple Algorithm ◽  
High Dimensional ◽  
Nonlinear Dimensionality Reduction ◽  
Topological Stability ◽  
Intrinsic Structure ◽  
Priori Information

Isomapis a classic and efficient manifold learning algorithm, which aims at finding the intrinsic structure hidden in high dimensional data. Only deficiency appeared in this algorithm is that it requires user to input a free parameterkwhich is closely related to the success of unfolding the true intrinsic structure and the algorithm’s topological stability. Here, we propose a novel and simplek-nn basedconcept: natural nearest neighbor (3N), which is independent of parameterk, so as to addressing the longstanding problem of how to automatically choosing the only free parameterkin manifold learning algorithms so far, and implementing completely unsupervised learning algorithm3N-Isomapfor nonlinear dimensionality reduction without the use of any priori information about the intrinsic structure. Experiment results show that3N-Isomapis a more practical and simple algorithm thanIsomap.

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