… A called sensible principal component analysis (SPCA) which de(… SPCA is also done with
an EM algorithm. I report results on synthetic and real data showing that these EM algorithms …

… principal component analysis (SPCA) for hyperspectral image feature extraction. The SPCA
… This research used GA as the search algorithm for the above SPCA process. The overall …

… constraints into the EM algorithm (see next section), … SPCA, PathSPCA and our algorithm
(called emPCA) on the face image data set. Variational renormalization is necessary for SPCA …

… The method is easily applied to kernel PCA. It is also shown that the new EM algorithm is …
The previous EM algorithm for PCA minimizes the squared error function monotonically. …

… the EM algorithm and its variational extension, and applies them to multinomial PCA. … a
mixture problem directly amenable to solution via the EM algorithm [9], but also simplified into a “K…

… EM-PCA algorithm. It seems that our EM algorithms are slightly slower than the EMPCA
algorithm in a first few iterations, since our EM algorithms tries to minimize the integrated-squared…

… We compare sPCA against the closest PCA implementations, … show that sPCA outperforms
both Mahout-PCA and MLlib-PCA by … EM is a well-known method to optimize the likelihood of …

… robust probabilistic PCA, and in Section 3 we describe the proposed Bayesian reformulation
of robust probabilistic PCA. In Section 4, we discuss the model fitting using EM algorithm …

… This chapter also brings in a model called sensible PCA SPCA) which delineates a
proper covariance structure in the data space as proposed by Roweis (1998) whose main …

… We have further extended the PPCA-EM algorithm to complex space to facilitate the application
to 3D reconstructions in different forms. Hence t i ∈ C d , for all i = 1 , 2 , … , n . In addition…