Nonnegative Local Coordinate Factorization for Image Representation

Chen, Yan; Zhang, Jiemi; Cai, Deng; Liu, Wei; He, Xiaofei

doi:10.1109/tip.2012.2224357

Cited by 89 publications

(38 citation statements)

References 31 publications

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“…(i) RNMF using l 2,1 norm [27] (ii) Semisupervised graph-regularized NMF (semi-GNMF) [24] (iii) Constrained NMF (CNMF) [16] (iv) Local centroid-structured NMF (LCSNMF) [47] (v) Unsupervised robust seminonnegative graph embedding through the l 2,1 norm (URNGE) [28] (vi) Nonnegative local coordinate factorization (NLCF) [9] (vii) Our proposed RSNLCF Sample images are shown in Figure 1.…”

Section: Compared Methodsmentioning

confidence: 99%

“…Accordingly, dimensionality reduction [5][6][7] is becoming increasingly important as it can overcome the curse of dimensionality, enhance the learning speed, and even offer critical insights into the essence of the issue. In general, dimensionality reduction methods can be divided into two categories: feature extraction [5,8,9] and selection [10][11][12][13][14]. Feature selection involves selecting discriminative and highly related features from an input feature set, whereas feature extraction combines original features to form new features of data variables.…”

Section: Introductionmentioning

confidence: 99%

“…Cai et al [24] extended NMF and proposed a graph-regularized NMF (GNMF) scheme, which imposes intrinsic geometry latent in a highdimensional dataset onto the traditional NMF using an affinity graph. Chen et al [9] presented a nonnegative local coordinate factorization (NLCF) method that imposes locality constraint onto the original NMF to explore faithful intrinsic geometry.…”

Section: Introductionmentioning

confidence: 99%

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Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Jiang

Yan

et al. 2018

Complexity

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Obtaining an optimum data representation is a challenging issue that arises in many intellectual data processing techniques such as data mining, pattern recognition, and gene clustering. Many existing methods formulate this problem as a nonnegative matrix factorization (NMF) approximation problem. The standard NMF uses the least square loss function, which is not robust to outlier points and noises and fails to utilize prior label information to enhance the discriminability of representations. In this study, we develop a novel matrix factorization method called robust semisupervised nonnegative local coordinate factorization by integrating robust NMF, a robust local coordinate constraint, and local spline regression into a unified framework. We use the l 2,1 norm for the loss function of the NMF and a local coordinate constraint term to make our method insensitive to outlier points and noises. In addition, we exploit the local and global consistencies of sample labels to guarantee that data representation is compact and discriminative. An efficient multiplicative updating algorithm is deduced to solve the novel loss function, followed by a strict proof of the convergence. Several experiments conducted in this study on face and gene datasets clearly indicate that the proposed method is more effective and robust compared to the state-of-the-art methods.

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Section: Compared Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Jiang

Yan

et al. 2018

Complexity

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“…In addition, two commonly used criteria [1,3,13,31] are adopted to measure the performance of clustering: clustering accuracy (AC) and normalized mutual information (NMI). The AC metric is defined as…”

Section: Evaluations and Analysesmentioning

confidence: 99%

Low rank approximation with sparse integration of multiple manifolds for data representation

Tao

Wang

et al. 2014

Appl Intell

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Manifold regularized techniques have been extensively exploited in unsupervised learning like matrix factorization whose performance is heavily affected by the underlying graph regularization. However, there exist no principled ways to select reasonable graphs under the matrix decomposition setting, particularly in multiple heterogeneous graph sources. In this paper, we deal with the issue of searching for the optimal linear combination space of multiple graphs under the low rank matrix approximation model. Specifically, efficient projection onto the probabilistic simplex is utilized to optimize the coefficient vector of graphs, resulting in the sparse pattern of coefficients. This attractive property of sparsity can be interpreted as a criterion for selecting graphs, i.e., identifying the most discriminative graphs and removing the noisy or irrelevant graphs, so as to boost the low rank decomposition performance. Experimental results over diverse popular image and web document L. Tao ( ) · H. corpora corroborate the effectiveness of our new model in terms of clusterings.

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“…LLDE apply the constrained weights to strengthen the classification ability. Y. Chen et al [34] present a local coordinate factorization (NLCF) method by adding a local coordinate constraint into the standard NMF objective function. Q. Gao et al [35] propose the stable orthogonal local discriminate embedding algorithm by introducing the orthogonal constraint on the basis vectors.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou

2016

J Wireless Com Network

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In this paper, a novel target recognition method, namely orthogonal maximum margin projection subspace (OMMPS), is proposed for radar target recognition using high-resolution range profile (HRRP). The core of OMMPS is to maximize the between-class margin by increasing the between-class scatter distance and reducing the within-class scatter distance simultaneously. By introducing the nonlinear mapping function, we also derive the kernel version of OMMPS, namely orthogonal kernel maximum margin projection subspace (OKMMPS). Compared with maximum margin criterion (MMC) method, OMMPS are optimal in meaning of maximum margin due that the coordinate axes of OMMPS are obtained sequentially by solving the constrained optimization problem, thus improves the recognition performance. In addition, the number of efficient features for OMMPS is not limited by the number of pattern classes, and the appropriate features can still be obtained for separating the classes, even in high-dimensional space with only a few classes. Moreover, the coordinate axes of OMMPS are mutually orthogonal, and the features extracted by OMMPS reduce the redundancy. The extensive experimental results show that the proposed method has better recognition performance than the other methods such as MMC and LDA.

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Nonnegative Local Coordinate Factorization for Image Representation

Abstract: Recently Non-negative Matrix Factorization (NMF)

Cited by 89 publications

References 31 publications

Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Low rank approximation with sparse integration of multiple manifolds for data representation

Orthogonal maximum margin projection subspace for radar target HRRP recognition

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