… sparse components with the same sparsity pattern. To this end, using Roweis’ probabilistic
interpretation of PCA … of the marginal likelihood of a Bayesian PCA model. In order to avoid …

… To this end, using Roweis’ probabilistic interpretation of PCA … the marginal likelihood
of a Bayesian PCA model. Moreover, in … this approach globally sparse probabilistic PCA (GSPPCA). …

… While PCA treats the PCs and the PC scores as fixed parameters, probabilistic PCA imposes
a prior on the PC … We queried the Global Biobank Engine for associations from our loci. The …

… Inspired by the sparse Bayesian Learning [16] and Bayesian … formulation of sparse PCA,
namely sparse probabilistic PCA (… a sparse probabilistic method and decomposed the global …

… We investigate the application of Probabilistic Principal Component Analysis (PPCA) to
manage high-dimensional sparse data in the stock market's equity domain, specifically using …

… We propose a unified approach to regularized PCA which can induce both sparsity and …
literature, with sparse, functional, two-way sparse, and two-way functional PCA all being special …

… two problems: sparse regression and principal component analysis (PCA). For sparse
regression, we … In the context of PCA, we attain optimal error guarantees under broad spikiness …

… scheme as the global BIC method. Although the asymptotic properties of the global BIC and
… the local BIC method typically performs better than the global BIC method (see Example 3 in …

… on probabilistic interpretations for sparse PCA [38], [39], a globally sparse probabilistic PCA
… a diagonal variable selection matrix to the probabilistic PCA formulation, as used in (1). The …

… In this paper, a Bayesian approach for globally sparse probabilistic principal component
analysis (PCA) is presented for unsupervised feature selection and damage identification using …