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SignCorrectionInSVDandPCA
虽然奇异值分解（SVD）和特征值分解（EVD的）是行之有效的，可以通过先进的设施设备先进的算法，它不是通常所说，有一个内在的迹象，可以显着影响的不确定性的结论计算及诠释来自其结果。我们提供一个解决方案，标志模糊的问题确定了从奇异向量的内积和个人数据载体签署奇异向量的迹象。该数据可能有不同的载体，但它有它自身的定位和实际意义的选择方向，其中多数的向量点。这可以通过评估发现了内心的签署标志产品的总和。(Although the Singular Value Decomposition (SVD) and eigenvalue decomposition (EVD) are well-established and can be computed via state-of-the-art algorithms, it is not commonly mentioned that there is an intrinsic sign indeterminacy that can significantly impact the conclusions and interpretations drawn from their results. We provide a solution to the sign ambiguity problem by determining the sign of the singular vector from the sign of the inner product of the singular vector and the individual data vectors. The data vectors may have different orientation but it makes intuitive as well as practical sense to choose the direction in which the majority of the vectors point. This can be found by assessing the sign of the sum of the signed inner products. )
- 2010-07-01 09:49:23下载
- 积分：1
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QPSKtransmitter
matlab code for qpsk
- 2009-07-11 22:45:24下载
- 积分：1
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K-meanCluster
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)
- 2007-11-15 01:49:03下载
- 积分：1
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newton-raphson
newton-raphson search method
optimization methodsd and algorithms
- 2014-11-27 22:04:07下载
- 积分：1
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7941933MATLABwindpower
programm that describe how control dfig
- 2009-10-10 16:01:03下载
- 积分：1
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S2
说明： transformer simulation file
- 2013-03-02 05:22:57下载
- 积分：1
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rough-set-reduct
粗糙集中用于数据约简的算法的matlab源代码(Rough set theory for data reduction algorithm matlab source code)
- 2010-11-23 15:20:48下载
- 积分：1
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ldpc_distr1
说明： 低密度码信道编译码算法，程序有信道模拟，matlab语言实现，主要研究规则低密度码(channel low-density code encryption algorithm, the procedure channel simulation, Matlab language, the main rules of low-density code)
- 2005-11-20 22:08:19下载
- 积分：1
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CRC32
CRC循环检测算法，CRC32的算法，可算得余数和商(CRC32 algorithm, and the remainder can be considered as business)
- 2011-06-09 11:12:23下载
- 积分：1
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bisp3cum
矢双谱的处理方法。方便快捷的更有效的方便信号处理(Vector processing method bispectrum. More convenient and effective to facilitate the signal processing)
- 2015-03-02 17:33:43下载
- 积分：1
