加拿大论文代写：因子分析
加拿大论文代写：因子分析
主成分分析与因子分析有着密切的关系。在典型意义上，因子分析包含了特定于域的关于基础结构的假设，而在矩阵上求解特征向量略有不同。此外，PCA得到的结果对变量尺度有关键依赖关系，而PCA的发展是以不变尺度的形式出现的(Abdi & Williams, 2010)。在推导主成分分析的过程中，由于假设的特殊性，存在一定的局限性。第一个假设是在线性方面，数据集将是每一个相关变量的线性组合。第二个假设是关于协方差和均值的意义。似乎没有任何保证，在指导最大方差，包括良好的属性歧视(Bowen & Guo, 2011)。第三个假设是关于具有显著动态的大方差。
这说明，具有较大方差的分量对应于关键动力学，而较小的分量对应于噪声。在统计术语中，使用varimax旋转来简化关于某些主要项目的特定子空间的表达式。实际的坐标系不会改变。不会发生任何变化，因为它是正交基，旋转以确保不同坐标之间的对齐(Bro & Smilde, 2014)。PCA子空间的发现可以表示为一个密度较大的基，具有多个非零权值，难以解释。Varimax是需要旋转，因为它有助于最大限度的总方差的平方负荷。载荷的平方可以确定为因素和变量之间的平方相关性。正交性的保持要求旋转保持子空间不变。
加拿大论文代写：因子分析
PCA has close relation with the analysis of factor. Analysis of factor, in the typical sense, incorporates assumptions specific to domain regarding the underlying structure, while solving eigenvectors over a matrix that is slightly different. In addition, the results obtained from PCA have key dependence upon the variables scales, while there is a development of PCA in form of invariant scale (Abdi & Williams, 2010). There is limitation of applicable PCA by specific assumption while drafting out its derivation.
The first presumption is in terms of linearity that data set will be linear combinations of each and every variable involved. The second presumption is regarding the significance of co- variance and mean. There does not seem to any guarantee in directing maximum variance that consists of good attributes for discrimination (Bowen & Guo, 2011). The third presumption is about large variances having significant dynamics.
This states that components having large variance will be corresponding with the crucial dynamics, while the lower ones will be corresponding with noise. In statistical terms, there is use of varimax rotation for simplifying the expression of a specific sub- space with respect to some major items. The actual system of coordinate will not change. No change will take place as it is the orthogonal base, rotating for ensuring alignment between different coordinates (Bro & Smilde, 2014). The discovery of sub- space with PCA can be expressed as a dense base with a number of non- zero weight, creating difficult interpretation. Varimax is needed as rotation as it helps in maximizing the total variances across the squared loadings. Squared loading can be identified as squared correlations between factors and variables. The preservation of orthogonality requires that the rotation will be leaving the invariant of sub- space.
