Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example Platform: |
Size: 454700 |
Author:东方云 |
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Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example Platform: |
Size: 454656 |
Author:东方云 |
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Description: 基于GM算法和QR分解实现的稳健奇异值分解算法，通过该算法可以获取一个数居阵的稳健方差－协方差阵。在该稳健方差－协方差阵上可以进行诸如，稳健主成份分解、稳健聚类、稳健因子分析等操作。-GM based on QR decomposition algorithm and the realization of the stability of singular value decomposition algorithm, the algorithm can be accessed through a number of UN-array sound variance- covariance matrix. In the robust variance- covariance matrix can be carried out, such as, robust principal component decomposition, robust clustering, robust operation, such as factor analysis. Platform: |
Size: 205824 |
Author:徐林林 |
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Description: Our toolbox currently contains implementations of robust methods for
location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-
LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi-
pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox
also provides many graphical tools to detect and classify the outliers. The use of these
features will be explained and demonstrated through the analysis of some real data
sets. Platform: |
Size: 294912 |
Author:王一 |
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Description: 一种健壮的主成分分析算法，根据文献《ROBPCA: a New Approach to Robust Principal Component Analysis》-《ROBPCA: a New Approach to Robust Principal Component Analysis》 Platform: |
Size: 9216 |
Author:钱叶魁 |
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Description: Deaf people use facial expressions a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade - Lucas - Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand.-Deaf people use facial expressions as a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade- Lucas- Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand. Platform: |
Size: 189440 |
Author:Ng Jack |
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Description: RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。该代码是求解RPCA的一种数值算法——Exact ALM（Exact Augmented Lagrange Multiplier）-The most basic form of the exact ALM function is [A, E] = exact_alm_rpca(D, λ), and that of the inexact ALM function is [A, E] = inexact_alm_rpca(D, λ), where D is a real matrix and λ is a positive real number. We solve the RPCA problem using the method of augmented Lagrange multipliers. The method converges Q-linearly to the optimal solution. The exact ALM algorithm is simple to implement, each iteration involves computing a partial SVD of a matrix the size of D, and converges to the true solution in a small number of iterations. The algorithm can be further speeded up by using a fast continuation technique, thereby yielding the inexact ALM algorithm. Platform: |
Size: 380928 |
Author:Bingmiao Huang |
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Description: RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。目前最有效的算法是ALM（Augmented Lagrange Multiplier）。ALM分为Exact ALM和Inexact ALM。 该代码是Inexact ALM，收敛速度比Exact ALM快！-RPCA (Robust Principal Component Analysis) is used for matrix filling, image denoising. It is currently the most effective optimization method. Currently the most effective method is ALM (Augmented Lagrange Multiplier). There re 2 kinds of ALM: Exact ALM and Inexact ALM. The code is Inexact ALM, faster convergence speed than the Exact ALM! Platform: |
Size: 380928 |
Author:Bingmiao Huang |
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Description: 介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。-Our toolbox currently contains implementations of robust
methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal
component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify
the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets. Platform: |
Size: 429056 |
Author:杨李 |
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Description: 鲁棒式主成分分析的实例，包含matlab程序，可以帮助理解鲁棒式主成分分析，也可以作为编程的参考。-Examples of robust type of principal component analysis, including matlab program that can help understand the type of robust principal component analysis, programming can also be used as a reference. Platform: |
Size: 2849792 |
Author:liudi |
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Description: 基于 Robust Principal Component Analysis（RPCA）理论，并结合小波多分辨率分析，提出一种新的流量矩
阵结构分析手段——多分辨率 RPCA。-Based on Principal Component Analysis Robust (RPCA) theory, and combined with the wavelet multi-resolution analysis, a new flow moment
Array structure analysis method: multi resolution RPCA. Platform: |
Size: 615424 |
Author:mafeng |
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Search - robust principal