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Non-negative matrix factorization (NMF) plays an important role in multivariate data analysis, and has been widely applied in information retrieval, computer vision, and pattern recognition. NMF is an effective method to capture the underlying structure of the data in the parts-based low dimensional representation space. However, NMF is actually an(More)
Concept factorization (CF) is a variant of non-negative matrix factorization (NMF). In CF, each concept is represented by a linear combination of data points, and each data point is represented by a linear combination of concepts. More specifically, each concept is represented by more than one data point with different weights, and each data point carries(More)
Chen et al. proposed a non-negative local coordinate factor-ization algorithm for feature extraction (NLCF) [1], which incorporated the local coordinate constraint into non-negative matrix factorization (NMF). However, NLCF is actually a un-supervised method without making use of prior information of problems in hand. In this paper, we propose a novel graph(More)
Non-negative Matrix Factorization (NMF) aims to find two non-negative matrices whose product approximates the original matrix well, and is widely used in clustering condition with good physical interpretability and universal applicability. Detecting communities with NMF can keep non-negative network physical definition and effectively capture(More)
Non-negative matrix factorization (NMF) is a very effective method for high dimensional data analysis, which has been widely used in information retrieval, computer vision, and pattern recognition. NMF aims to find two non-negative matrices whose product approximates the original matrix well. It can capture the underlying structure of data in the low(More)
Hashing for large scale similarity search has become more and more popular because of its improvement in computational speed and storage reduction. Semi-supervised Hashing (SSH) has been proven effective since it integrates both labeled and unlabeled data to leverage semantic similarity while keeping robust to overfitting. However, it ignores the global(More)
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