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Description: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.-Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA (X, PPCA_DIM) computes the principal component subspace U of dimension PPCA_DIM using a centred covariancematrix X. The variable VAR contains the off-subspace variance (whichis assumed to be spherical ), while the vector LAMBDA contains thevariances of each of the principal components. This is computedusing the eigenvalue and eigenvector decomposition of X.
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Size: 1024 |
Author: 西晃云 |
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Description: 基于分数低阶协方差仿真的时延估计问题，其基本思想是利用两个接收信号的相关函数来估计时间延迟。-Based on the scores of low-level simulation of time delay covariance estimation problem, the basic idea is to use of the two received signal correlation function to estimate the time delay.
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Size: 1024 |
Author: 阿岩 |
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Description: 计算多维正态分布的概率密度值，给出均值和方差矩阵-Multidimensional normal distribution calculate the probability density values, given the mean and covariance matrix
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Size: 2048 |
Author: zhwt |
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Description: ACF中包含两个m文件，一个是自协方差函数（可单独使用）。另一个是自相关函数计算，其调用autocov函数。-ACF contains two m files, one is self-covariance function (which can be used alone). The other is calculated from the correlation function, which calls autocov function.
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Size: 2048 |
Author: zxhitler |
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Description: The number of states in GMM as the generative model of the frames is obtained using
k-means algorithm. This also helps to initialize the mean vector and the covariance
matrix of the individual state of the GMM. The training LPC frames collected from
three speech segments are subjected to PCA for dimensionality reduction and are
subjected to k-means algorithm. The total number of frames is equal to the total
number of vectors that are subjected to k-means clustering.
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Size: 728064 |
Author: Khan17
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