Generalized Principal Component Analysis (GPCA): an Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation by
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چکیده
Generalized Principal Component Analysis (GPCA): an Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation
منابع مشابه
Estimation of Subspace Arrangements: Its Algebra and Statistics by Allen
In the literature of computer vision and image processing, a fundamental difficulty in modeling visual data is that multivariate image or video data tend to be heterogeneous or multimodal. That is, subsets of the data may have significantly different geometric or statistical properties. For example, image features from multiple independently moving objects may be tracked in a motion sequence, o...
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Generalized Principal Component Analysis (GPCA) and Probabilistic Principal Component Analysis (PPCA) are two extensions of PCA approaches to the mixtures of principal subspaces. GPCA is an algebraic geometric framework in which the collection of linear subspaces is represented by a set of homogeneous polynomials whose degree corresponds to the number of subspaces and whose factors (roots) enco...
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