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Description: Multilinear Principal Component Analysis
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Size: 2088174 |
Author: 孤松 |
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Description: Multilinear Principal Component Analysis
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Size: 2087936 |
Author: 孤松 |
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Description: 高维PCA
参考文献：
MPCA Multilinear Principal Component Analysis of Tensor Objects-High-dimensional PCA References: MPCA Multilinear Principal Component Analysis of Tensor Objects
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Size: 3072 |
Author: wzh |
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Description: signal processing based on multilinear algebra，国外的学者的博士论文，主题关于信号处理最前沿的课题之一：平行因子分解（PARAFAC）。主要讨论了多重线性代数，包括高阶统计量分析，高阶奇异值分解，最优秩1分解，ICA与PARAFAC之间的关系等等。-signal processing based on multilinear algebra, foreign doctoral dissertations and scholars, the subject of signal processing on the forefront of one of the topics: the parallel factorization (PARAFAC). Focused on multi-linear algebra, including the analysis of higher-order statistics, higher-order singular value decomposition, optimal rank 1 decomposition, ICA and the relationship between PARAFAC and so on.
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Size: 1492992 |
Author: 云上 |
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Description: The matlab codes provided here implement two algorithms multilinear principal component analysis to run the face recognition using FERET database.
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Size: 2073600 |
Author: nick |
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Description: 编程主要用于脸部特征提取，而且是三维图像。-Multilinear principal component analysis algorithm for face feature extraction.
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Size: 1548288 |
Author: tai shu worn |
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Description: 编程是由pca和lda结合的脸部特征提取，用于三维图像。-multilinear principal component analysis combined with Linear discriminant analysis 3D face feature extraction.
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Size: 4096 |
Author: tai shu worn |
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Description: Multilinear Principal Component Analysis
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Size: 2088960 |
Author: Mike |
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Description: 如何使用Multilinear Principal Component Analysis-learn how to Multilinear Principal Component Analysis
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Size: 529408 |
Author: meng |
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Description: MPCA Multilinear Principal Component Analysis of Tensor Objects with MATLAB
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Size: 2073600 |
Author: javad |
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Description: 多线性主成分分析的MATLAB源代码
最初用于人脸识别和步态识别
后被扩展到其他应用领域-The codes implement two algorithms: Multilinear Principal Component Analysis (MPCA) and MPCA+LDA.
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Size: 3332096 |
Author: Lu Haiping |
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Description: 多主成分分析和LDA模型结合算法，用于文本识别。-MPCA: Multilinear Principal Component
Analysis of Tensor Objects
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Size: 3072 |
Author: 孙磊 |
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Description: The codes implement two algorithms Multilinear Principal Component Analysis (MPCA) and MPCA+LDA
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Size: 8192 |
Author: gogo |
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Description: 多线性交换子-算法Multilinear commutator - Algorithm-Multilinear commutator- Algorithm
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Size: 686080 |
Author: chenzhanyi |
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Description: Special attention is given to the development of algorithms
for image formation from raw data. Kronecker algebra has
been used, as a tool aid for reducing the computational effort
in the MATLAB?implementation process of unitary
operators, such as the multidimensional discrete Fourier
transform, which form an integral part of some of these
algorithms. The MATLAB?environment, named SARCSPE, is
described in a finite dimensional multilinear algebra
framewor
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Size: 408576 |
Author: yas |
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Description: 高维PCA参考文献：MPCA Multilinear Principal Component Analysis of Tensor Objects-High-dimensional PCA References: MPCA Multilinear Principal Component Analysis of Tensor Objects
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Size: 4096 |
Author: centyear |
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Description: ansys多线性各向同性，多线性随动硬化-ANSYS linear, isotropic, multilinear kinematic hardening
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Size: 2048 |
Author: 李刚 |
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Description: 张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature
Extraction and Applications-
Data in modern applications such as BCI based on EEG signals often contain multi-modes due to
mechanism of data recording, e.g. signals recorded by multiple-sensors (electrodes), in multiple trials,
epochs, for multiple subjects and with different tasks, conditions. Moreover, during processing and
analysis, dimensionality of the data could be augmented due to expression of the data into sparse
domain (time-frequency representation) by different transforms such as STFT, wavelets. That means
data itself is naturally a tensor, and has multilinear structures. Standard approaches which analyze
such data by considering them as vectors or matrices might be not suitable due to risk of losing the
covariance information among various modes. To discover hidden multilinear structures, features
within the data, the analysis tools should reflect the multi-dimensional structure of the data
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Size: 2438144 |
Author: 李新会 |
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