Subspace Evolution and Transfer (SET) for Low-Rank Matrix Completion
نویسندگان
چکیده
منابع مشابه
Low-Rank Matrix Completion
While datasets are frequently represented as matrices, real-word data is imperfect and entries are often missing. In many cases, the data are very sparse and the matrix must be filled in before any subsequent work can be done. This optimization problem, known as matrix completion, can be made well-defined by assuming the matrix to be low rank. The resulting rank-minimization problem is NP-hard,...
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In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix ...
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In this paper, we describe a low-rank matrix completion method based on matrix decomposition. An incomplete matrix is decomposed into submatrices which are filled with a proposed trimming step and then are recombined to form a low-rank completed matrix. The divide-and-conquer approach can significantly reduce computation complexity and storage requirement. Moreover, the proposed decomposition m...
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Subspace clustering is the problem of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This type of structure occurs naturally in many applications ranging from bioinformatics, image/text clustering to semi-supervised learning. The companion paper [3] shows that robust and tractable subspace clustering is possible with minimal re...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2011
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2011.2144977