Filters








4,196,362 Hits in 4.3 sec

Local Component Analysis [article]

Nicolas Le Roux, Francis Bach
2012 arXiv   pre-print
In this paper, we propose to learn a full Euclidean metric through an expectation-minimization (EM) procedure, which can be seen as an unsupervised counterpart to neighbourhood component analysis (NCA)  ...  component analysis (LCA).  ...  This algorithm, which we coin LCA, for local component analysis, transforms the data to make it locally isotropic, as opposed to PCA which makes it globally isotropic.  ... 
arXiv:1109.0093v4 fatcat:xckz43vcdjfjvezavrplc7ej6y

Topological local principal component analysis

Zhi-Yong Liu, Lei Xu
2003 Neurocomputing  
In help of the Kohonen's self-organizing maps we present a topological local principal component analysis model which is capable of exploiting both the global topological structure and each local cluster  ...  Introduction Principal component analysis (PCA) [6] is frequently adopted for dimensionality reduction as the mean square error (MSE) upon reconstruction is minimized.  ...  It follows that 1 ; : : : ; m are the ÿrst m principal components of . Thus, we can study the local PCA by estimating the gaussian mixture model with the covariance matrix given by Eq. (2) .  ... 
doi:10.1016/s0925-2312(03)00414-4 fatcat:ill4vziadvf3xcs6knoxu3svmq

Local functional principal component analysis [article]

André Mas
2007 arXiv   pre-print
The principal component analysis of a random function X is well-known from a theoretical viewpoint and extensively used in practical situations. In this work we focus on local covariance operators.  ...  Asymptotic developments are given under assumptions on the location of x₀ and on the distributions of projections of the data on the eigenspaces of the (non-local) covariance operator.  ...  In this article we focus on a very useful and common statistical technique : principal component analysis (PCA for short).  ... 
arXiv:math/0702609v1 fatcat:axae7vqnond3vijhal7ejzjn7i

Localized Functional Principal Component Analysis

Kehui Chen, Jing Lei
2015 Journal of the American Statistical Association  
We propose localized functional principal component analysis (LFPCA), looking for orthogonal basis functions with localized support regions that explain most of the variability of a random process.  ...  The first localized basis function ϕ 1 (t), explaining 54% of the total variance, indicates that a big variation of the mortality functions X i (t) around their  ...  In this paper, we consider functional principal component analysis with localized support regions.  ... 
doi:10.1080/01621459.2015.1016225 pmid:26806987 pmcid:PMC4721272 fatcat:v3e4vg66l5a7xpizqpkjchjxl4

Local Functional Principal Component Analysis

André Mas
2007 Complex Analysis and Operator Theory  
The principal component analysis of a random function X is well-known from a theoretical viewpoint and extensively used in practical situations. In this work we focus on local covariance operators.  ...  Asymptotic developments are given under assumptions on the location of x 0 and on the distributions of projections of the data on the eigenspaces of the (non-local) covariance operator.  ...  In this article we focus on a very useful and common statistical technique : principal component analysis (PCA for short).  ... 
doi:10.1007/s11785-007-0026-x fatcat:rwxth4754jh7zjzomf6hs6xt54

Localized Functional Principal Component Analysis [article]

Kehui Chen, Jing Lei
2015 arXiv   pre-print
We propose localized functional principal component analysis (LFPCA), looking for orthogonal basis functions with localized support regions that explain most of the variability of a random process.  ...  The LFPCA is formulated as a convex optimization problem through a novel Deflated Fantope Localization method and is implemented through an efficient algorithm to obtain the global optimum.  ...  In this paper, we consider functional principal component analysis with localized support regions.  ... 
arXiv:1501.04933v1 fatcat:h4563524dnb5nn7he74abgl73e

Local Livelock Analysis of Component-Based Models [chapter]

Madiel S. Conserva Filho, Marcel Vinicius Medeiros Oliveira, Augusto Sampaio, Ana Cavalcanti
2016 Lecture Notes in Computer Science  
Our method is based solely on the local analysis of the minimum sequences that lead the CSP model back to its initial state.  ...  In this case, we carry out livelock analysis in the context of a component model, BR I C , whose behaviour of the components is described as a CSP process.  ...  SLAP, and (3) our local livelock analysis.  ... 
doi:10.1007/978-3-319-47846-3_18 fatcat:mdrjldtqcvhw5i6zfxidi5vaxu

Local Component Analysis for Nonparametric Bayes Classifier [article]

Mahmoud Khademi, Mohammad T. Manzuri-Shalmani, Meharn safayani
2012 arXiv   pre-print
In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis.  ...  In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information  ...  REVIEW OF SUBSPACE LEARNING ALGORITHM Two of the traditional techniques for dimension reduction are principal component analysis (PCA) and linear discriminant analysis (LDA) [1] .  ... 
arXiv:1010.4951v2 fatcat:tvoxd7bpy5d27fwzdvv5y44bve

Independent component analysis for EEG source localization

L. Zhukov, D. Weinstein, C. Johnson
2000 IEEE Engineering in Medicine and Biology Magazine  
The EEG data is first decomposed into signal and noise subspaces using a Principal Component Analysis (PCA) decomposition.  ...  After PCA, we apply Independent Component Analysis (ICA) on the signal subspace. The ICA algorithm separates multichannel data into activation maps due to temporally independent stationary sources.  ...  We begin by extracting the signal subspace of the EEG data using a Principal Component Analysis (PCA) algorithm.  ... 
doi:10.1109/51.844386 pmid:10834122 fatcat:nd6dsjs5obfzhgdvzbf2wzlrdm

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis [article]

Chelsea Weaver, Naoki Saito
2018 arXiv   pre-print
Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training  ...  The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors.  ...  Proposed Algorithm Local Principal Component Analysis Sparse Representation-Based Classification Our proposed algorithm, local principal component analysis sparse representation-based classification  ... 
arXiv:1607.01059v6 fatcat:s6t6k5xarngerk4yxt4mgp2w3u

Independent Component Analysis for Source Localization of EEG Sleep Spindle Components

Erricos M. Ventouras, Periklis Y. Ktonas, Hara Tsekou, Thomas Paparrigopoulos, Ioannis Kalatzis, Constantin R. Soldatos
2010 Computational Intelligence and Neuroscience  
The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG  ...  and visual selection of Independent Components (ICs), spindle "components" (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying  ...  (SNIP-I.F.: 0.271) 49) (CP19) E.C.Ventouras, M.Moatsos, C.Papageorgiou, A.Rabavilas, N.Uzunoglu, "Independent Component Analysis applied to the P600 Component of Event-Related Potentials".  ... 
doi:10.1155/2010/329436 pmid:20369057 pmcid:PMC2847376 fatcat:hkwqlv254jfghiorpqc5sodavm

A Local Learning Rule for Independent Component Analysis

Takuya Isomura, Taro Toyoizumi
2016 Scientific Reports  
This decomposition is mathematically formulated as independent component analysis (ICA).  ...  Independent component analysis (ICA) 9 is a mathematical model of BSS, where an observer receives linear mixtures of independent sources as inputs and determines the transformation back into their original  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.1038/srep28073 pmid:27323661 pmcid:PMC4914970 fatcat:43nzb7fazbc47ikgs733yymgau

LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING

JUHA KARHUNEN, SIMONA MĂlĂROIU, MIKA ILMONIEMI
2000 International Journal of Neural Systems  
In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data.  ...  These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.  ...  Local Independent Component Analysis The proposed local ICA method We propose the use of local linear ICA models in order to represent nonlinearly distributed data better than using a single global linear  ... 
doi:10.1142/s0129065700000429 pmid:11307858 fatcat:hfayqbfhajhkzn4qnvbfjjaoxa

A Local Operation Method Using Principal Component Analysis

Kosuke MORIWAKI, Tadashi HASHIMOTO, Seiji INOKUCHI
1989 Transactions of the Institute of Systems Control and Information Engineers  
今 後 は,多 属 性 画 像 の 多 面 的 な 解 釈 の ツ ー ル と して, Fig Fig. 2 Reration of local area's position and its distribution form in the variable space  ... 
doi:10.5687/iscie.2.283 fatcat:pnqe6wrofzcb5irgb3zjljy6gm

Exploration of Shape Variation Using Localized Components Analysis

D.A. Alcantara, O. Carmichael, W. Harcourt-Smith, K. Sterner, S.R. Frost, R. Dutton, P. Thompson, E. Delson, N. Amenta
2009 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components.  ...  In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible  ...  that spatially localized shape components will be optimal for every shape analysis task.  ... 
doi:10.1109/tpami.2008.287 pmid:19542583 pmcid:PMC2864033 fatcat:acp4k6fxfbf2hgbjppn7pmszdy
« Previous Showing results 1 — 15 out of 4,196,362 results