Ubiquitous data are increasingly expanding in large volumes due to human activities, and grouping them into appropriate clusters is an important and yet challenging problem. Existing matrix factorization techniques ha...
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Ubiquitous data are increasingly expanding in large volumes due to human activities, and grouping them into appropriate clusters is an important and yet challenging problem. Existing matrix factorization techniques have shown their significant power in solving this problem, e.g., nonnegative matrix factorization, concept factorization. Recently, one state-of-the-art method called locality-constrained concept factorization is put forward, but its locality constraint does not well reveal the intrinsic data structure since it only requires the concept to be as close to the original data points as possible. To address this issue, we present a graph-based local concept coordinate factorization (GLCF) method, which respects the intrinsic structure of the data through manifold kernel learning in the warped Reproducing Kernel Hilbert Space. Besides, a generalized update algorithm is developed to handle data matrices containing both positive and negative entries. Since GLCF is essentially based on the local coordinate coding and concept factorization, it inherits many advantageous properties, such as the locality and sparsity of the data representation. Moreover, it can better encode the locally geometrical structure via graph Laplacian in the manifold adaptive kernel. Therefore, a more compact and better structured representation can be obtained in the low-dimensional data space. Extensive experiments on several image and gene expression databases suggest the superiority of the proposed method in comparison with some alternatives.
Person re-identification (PRID) is a challenging problem in multi-camera surveillance systems. In this paper, we propose a novel Datum-Adaptive local Metric learning method for PRID, which learns individual local feat...
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Person re-identification (PRID) is a challenging problem in multi-camera surveillance systems. In this paper, we propose a novel Datum-Adaptive local Metric learning method for PRID, which learns individual local feature projection for each image sample according to the current data distribution and projects all samples into a common discriminative space for similarity measure. We adopt an approximate strategy based on local coordinate coding to learn local projections. Anchor points are first generated by clustering and the local projection of each sample is then approximated by the linear combination of a set of projection bases, which are associated with the anchor points. Experimental results demonstrate that the proposed approach obtains superior performance compared with state-of-the-art methods on public benchmarks.
Chen et al. proposed a non-negative localcoordinate factorization algorithm for feature extraction (NLCF) [1], which incorporated the localcoordinate constraint into non-negative matrix factorization (NMF). However,...
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ISBN:
(纸本)9781479970827
Chen et al. proposed a non-negative localcoordinate factorization algorithm for feature extraction (NLCF) [1], which incorporated the localcoordinate constraint into non-negative matrix factorization (NMF). However, NLCF is actually a unsupervised method without making use of prior information of problems in hand. In this paper, we propose a novel graph regularized non-negative localcoordinate factorization with pairwise constraints algorithm (PCGNLCF) for image representation. PCGNLCF incorporates pairwise constraints and graph Laplacian into NLCF. More specifically, we expect that data points having pairwise must-link constraints will have the similar coordinates as much as possible, while data points with pairwise cannot-link constraints will have distinct coordinates as much as possible. Experimental results show the effectiveness of our proposed method in comparison to the state-of-the-art algorithms on several real-world applications.
Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1) it is n...
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ISBN:
(数字)9783319265353
ISBN:
(纸本)9783319265353;9783319265346
Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1) it is non-trivial to guarantee the representation of the data points to be sparse, and (2) NMF often achieves unsatisfactory clustering results because it completely neglects the labels of the dataset. Thus, this paper proposes a semi-supervised non-negative localcoordinate factorization (SNLCF) to overcome the above deficiencies. Particularly, SNLCF induces the sparse coefficients by imposing the localcoordinate constraint and propagates the labels of the labeled data to the unlabeled ones by indicating the coefficients of the labeled examples to be the class indicator. Benefit from the labeled data, SNLCF can boost NMF in clustering the unlabeled data. Experimental results on UCI datasets and two popular face image datasets suggest that SNLCF outperforms the representative methods in terms of both average accuracy and average normalized mutual information.
Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in ma...
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Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in many previous works. We review this assumption and claim that using the similarity of data points to a more global set of anchor points does not necessarily weaken the coding method, as long as the underlying structure of the anchor points is considered. We propose to capture the underlying structure by assuming a random walker over the anchor points. We also show that our method is a fast approximation to the diffusion map kernel. Experiments on various data sets show that with a knowledge of the underlying structure of anchor points, different state-of-the-art coding algorithms may boost their performance in different learning tasks by utilizing the proposed method.
In this paper, we propose to address online visual tracking on the basis of local coordinate coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative ...
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ISBN:
(纸本)9781479947614
In this paper, we propose to address online visual tracking on the basis of local coordinate coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the localcoordinate codes of image patches to identify the foreground patches from background. In the generative module, we introduce a similarity function that takes the spatial structures of local patches in the target into account between the candidate and holistic templates by reconstruction error. To deal with appearance change during tracking, an online update method is introduced. The proposed tracking method is evaluated on different challenging video sequences with center location error, and experimental results demonstrate the good performance of our method.
Recently, nonnegative matrix factorization (NMF) has become increasingly popular for feature extraction in computer vision and pattern recognition. NMF seeks two nonnegative matrices whose product can best approximate...
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Recently, nonnegative matrix factorization (NMF) has become increasingly popular for feature extraction in computer vision and pattern recognition. NMF seeks two nonnegative matrices whose product can best approximate the original matrix. The nonnegativity constraints lead to sparse parts-based representations that can be more robust than nonsparse global features. To obtain more accurate control over the sparseness, in this paper, we propose a novel method called nonnegative localcoordinate factorization (NLCF) for feature extraction. NLCF adds a localcoordinate constraint into the standard NMF objective function. Specifically, we require that the learned basis vectors be as close to the original data points as possible. In this way, each data point can be represented by a linear combination of only a few nearby basis vectors, which naturally leads to sparse representation. Extensive experimental results suggest that the proposed approach provides a better representation and achieves higher accuracy in image clustering.
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