scholarly journals Characterizing neural phase-space trajectories via Principal Louvain Clustering

2021 ◽  
Author(s):  
Mark M. Dekker ◽  
Arthur S. C. França ◽  
Debabrata Panja ◽  
Michael X Cohen
Keyword(s):  
Field Potential ◽  
High Dimensional ◽  
Time Varying ◽  
Ongoing Behavior ◽  
Reduction Methods ◽  
Novel Method ◽  
High Consistency ◽  
Low Dimensional ◽  
High Dimensional Datasets ◽  
Local Field Potential Lfp

AbstractBackgroundWith the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges.New MethodIn this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments.ResultsPLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals’ ongoing behavior.ConclusionsPLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

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 Locally Linear Discriminate Embedding for Face Recognition

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Eimad E. Abusham ◽  
E. K. Wong
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Linear Structure ◽  
Data Representation ◽  
High Dimensional ◽  
Fast Processing ◽  
Novel Method ◽  
Computational Simplicity ◽  
Low Dimensional ◽  
Locally Linear

A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE). LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional) before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF) classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.

Soft Sensing Based on Kernel Isomap

2013 ◽  
Vol 278-280 ◽  
pp. 1349-1352
Author(s):  
Qiang Wang ◽  
Xue Min Tian
Keyword(s):  
Diesel Oil ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Support Vector ◽  
Soft Sensing ◽  
Regression Modelling ◽  
Vector Machines ◽  
Novel Method ◽  
Solidifying Point ◽  
Low Dimensional

A novel method of soft sensing is propsed combined Kernel Isomap (KIsomap) with Least squares support vector machines (LS-SVM). KIsomap is an improved Isomap and has a generalization property by utilizing kernel trick. It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data. The KIsomap is used to feature extraction and reduce dimensions of sample. The LSSVM is applied to proceed regression modelling, which can not only reduce the complexity of modeling but also improve the generalization ability.The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that KIsomap-LSSVM approach is effective and correct.

Nonlinear and Time-Varying Dynamics of High-Dimensional Models of a Translating Beam With a Stationary Load Subsystem

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
G. Y. Xu ◽  
W. D. Zhu
Keyword(s):  
Harmonic Balance ◽  
Harmonic Balance Method ◽  
High Dimensional ◽  
Structural Systems ◽  
Time Varying ◽  
Incremental Harmonic Balance Method ◽  
Included Trial ◽  
Incremental Harmonic Balance ◽  
Dimensional Models ◽  
Low Dimensional

Nonlinear vibration and dynamic stability analyses of distributed structural systems have often been conducted for their low-dimensional spatially discretized models, and the results obtained from the low-dimensional models may not accurately represent the behaviors of the distributed systems. In this work the incremental harmonic balance method is used to handle a variety of problems pertaining to determining periodic solutions of high-dimensional models of distributed structural systems. The methodology is demonstrated on a translating tensioned beam with a stationary load subsystem and some related systems. With sufficient numbers of included trial functions and harmonic terms, convergent and accurate results are obtained in all the cases. The effect of nonlinearities due to the vibration-dependent friction force between the translating beam and the stationary load subsystem, which results from nonproportionality of the load parameters, decreases as the number of included trial functions increases. A low-dimensional spatially discretized model of the nonlinear distributed system can yield quantitatively and qualitatively inaccurate predictions. The methodology can be applied to other nonlinear and/or time-varying distributed structural systems.

Non-Negative Based Locally Sparse Representation for Classification

2013 ◽  
Vol 677 ◽  
pp. 502-507
Author(s):  
Kang Hua Hui ◽  
Chun Li Li ◽  
Xiao Rong Feng ◽  
Xue Yang Wang
Keyword(s):  
Sparse Representation ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Discrimination Power ◽  
Knn Classifier ◽  
Reduction Methods ◽  
Linear Embedding ◽  
Low Dimensional

In this paper, a new method is proposed, which can be considered as the combination of sparse representation based classification (SRC) and KNN classifier. In detail, with the assumption of locally linear embedding coming into existence, the proposed method achieves the classification goal via non-negative locally sparse representation, combining the reconstruction property and the sparsity of SRC and the discrimination power included in KNN. Compared to SRC, the proposed method has obvious discrimination and is more acceptable for the real image data without those preconditions difficult to satisfy. Moreover, it is more suitable for the classification of low dimensional data dimensionally reduced by dimensionality reduction methods, especially those methods obtaining the low dimensional and neighborhood preserving embeddings of high dimensional data. The experiments on MNIST is also presented, which supports the above arguments.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit

2017 ◽  
Vol 27 (06) ◽  
pp. 1750024 ◽  
Author(s):  
Héctor Quintián ◽  
Emilio Corchado
Keyword(s):  
Hebbian Learning ◽  
Projection Pursuit ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Learning Rules ◽  
Low Dimensional ◽  
Exploratory Projection Pursuit ◽  
High Dimensional Datasets

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data’s internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of “interesting” dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

scholarly journals Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1646
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Multidimensional Scaling ◽  
Damage Detection ◽  
Health Monitoring ◽  
Large Scale ◽  
Environmental Variability ◽  
Data Driven ◽  
High Dimensional ◽  
Data Driven Approach ◽  
Low Dimensional ◽  
High Dimensional Datasets

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets

2018 ◽  
Vol 24 (1) ◽  
pp. 236-245 ◽  
Author(s):  
Jiazhi Xia ◽  
Fenjin Ye ◽  
Wei Chen ◽  
Yusi Wang ◽  
Weifeng Chen ◽  
...  
Keyword(s):  
Exploratory Analysis ◽  
High Dimensional ◽  
Low Dimensional ◽  
High Dimensional Datasets

Nonlinear and Time-Varying Dynamics of High-Dimensional Models of a Translating Beam With a Stationary Load Subsystem

2008 ◽  
Author(s):  
G. Y. Xu ◽  
W. D. Zhu
Keyword(s):  
High Dimensional ◽  
Structural Systems ◽  
Nonlinear Frequency ◽  
Time Varying ◽  
Included Trial ◽  
Frequency Responses ◽  
Linear And Nonlinear ◽  
Dimensional Models ◽  
Low Dimensional

Nonlinear vibration and dynamic stability analyses of distributed structural systems have often been conducted for their low-dimensional spatially-discretized models, and the results obtained from the low-dimensional models may not accurately represent the behavior of the distributed systems. In this work the incremental harmonic balance method is used for the first time to handle a variety of problems for high-dimensional models of distributed structural systems, including determination of linear and nonlinear frequency responses, optimization of system parameters, determination of simple parametric instability region boundaries, analysis of parametrically-excited nonlinear systems, and determination of linear and nonlinear frequency responses under combined parametric and forcing excitations. The methodology is demonstrated on a translating tensioned beam with a stationary load subsystem and some related systems. With sufficient numbers of included trial functions and harmonic terms, convergent and accurate results are obtained in all the cases. The effect of nonlinearities due to the vibration-dependent friction force between the translating beam and the stationary load subsystem, which results from non-proportionarity of the load parameters, decreases as the number of included trial functions increases. A low-dimensional spatially-discretized model of the nonlinear distributed system can yield quantitatively and qualitatively inaccurate predictions. The methodology can be applied to other nonlinear and/or time-varying distributed structural systems.

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