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.

scholarly journals Nonlinear dimensionality reduction for the acoustic field measured by a linear sensor array

2019 ◽  
Vol 283 ◽  
pp. 07009
Author(s):  
Xinyao Zhang ◽  
Pengyu Wang ◽  
Ning Wang
Keyword(s):  
Dimensionality Reduction ◽  
Acoustic Field ◽  
Degrees Of Freedom ◽  
Sensor Array ◽  
Learning Algorithm ◽  
High Dimensional ◽  
Compact Representation ◽  
Nonlinear Dimensionality Reduction ◽  
Complex Data ◽  
Local Tangent

Dimensionality reduction is one of the central problems in machine learning and pattern recognition, which aims to develop a compact representation for complex data from high-dimensional observations. Here, we apply a nonlinear manifold learning algorithm, called local tangent space alignment (LTSA) algorithm, to high-dimensional acoustic observations and achieve nonlinear dimensionality reduction for the acoustic field measured by a linear senor array. By dimensionality reduction, the underlying physical degrees of freedom of acoustic field, such as the variations of sound source location and sound speed profiles, can be discovered. Two simulations are presented to verify the validity of the approach.

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.

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.

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.

scholarly journals Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Azadeh Rezazadeh Hamedani ◽  
Mohammad Hossein Moattar ◽  
Yahya Forghani
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Geodesic Distance ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Latent Space ◽  
Space Modeling

AbstractDissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.

Polynomial approximation to manifold learning

2021 ◽  
pp. 1-19
Author(s):  
Guo Niu ◽  
Zhengming Ma ◽  
Haoqing Chen ◽  
Xue Su
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Polynomial Approximation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Dimensional Representation ◽  
Object Function ◽  
Polynomial Representation ◽  
Intrinsic Structure ◽  
Low Dimensional

Manifold learning plays an important role in nonlinear dimensionality reduction. But many manifold learning algorithms cannot offer an explicit expression for dealing with the problem of out-of-sample (or new data). In recent, many improved algorithms introduce a fixed function to the object function of manifold learning for learning this expression. In manifold learning, the relationship between the high-dimensional data and its low-dimensional representation is a local homeomorphic mapping. Therefore, these improved algorithms actually change or damage the intrinsic structure of manifold learning, as well as not manifold learning. In this paper, a novel manifold learning based on polynomial approximation (PAML) is proposed, which learns the polynomial approximation of manifold learning by using the dimensionality reduction results of manifold learning and the original high-dimensional data. In particular, we establish a polynomial representation of high-dimensional data with Kronecker product, and learns an optimal transformation matrix with this polynomial representation. This matrix gives an explicit and optimal nonlinear mapping between the high-dimensional data and its low-dimensional representation, and can be directly used for solving the problem of new data. Compare with using the fixed linear or nonlinear relationship instead of the manifold relationship, our proposed method actually learns the polynomial optimal approximation of manifold learning, without changing the object function of manifold learning (i.e., keeping the intrinsic structure of manifold learning). We implement experiments over eight data sets with the advanced algorithms published in recent years to demonstrate the benefits of our algorithm.

A New Supervised Manifold Learning Algorithm Based on MMC and LSE

2014 ◽  
Vol 39 (12) ◽  
pp. 2077-2089
Author(s):  
Min YUAN ◽  
Lei CHENG ◽  
Ran-Gang ZHU ◽  
Ying-Ke LEI
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm

Generalized incremental manifold learning algorithm based on local smoothness

2013 ◽  
Vol 32 (6) ◽  
pp. 1670-1673
Author(s):  
Xue-yan ZHOU ◽  
Jian-min HAN ◽  
Yu-bin ZHAN
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Local Smoothness

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

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