منابع مشابه
Probabilistic Principal Component Analysis
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متن کاملSparse Probabilistic Principal Component Analysis
Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through adding an L1 regularizer. In this paper, we introduce a probabilistic formulation for sparse PCA. By presenting sparse PCA as a p...
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Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable. In th...
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We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...
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Reviews of medication from patients are numerous on the internet. Mining significant topics from short and noisy reviews poses significant challenges. In this work, we addressed such problem by proposing the regressional probabilistic principal component analysis (RPPCA) to correlate the sentiment values of the reivew while simultaneously optimizing the probabilistic generative process of words...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 1999
ISSN: 1369-7412,1467-9868
DOI: 10.1111/1467-9868.00196