Graph-based adaptive and discriminative subspace learning for face image clustering

Discriminative sparse subspace learning and its application to unsupervised feature selection

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject’s age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.

Many subspace learning methods based on low-rank representation employ the nearest neighborhood graph to preserve the local structure. However, in these methods, the nearest neighborhood graph is a binary matrix, which fails to precisely capture the similarity between distinct samples. Additionally, these methods need to manually select an appropriate number of neighbors, and they cannot adaptively update the similarity graph during projection learning. To tackle these issues, we introduce Discriminative Subspace Learning with Adaptive Graph Regularization (DSL_AGR), an innovative unsupervised subspace learning method that integrates low-rank representation, adaptive graph learning and nonnegative representation into a framework. DSL_AGR introduces a low-rank constraint to capture the global structure of the data and extract more discriminative information. Furthermore, a novel graph regularization term in DSL_AGR is guided by nonnegative representations to enhance the capability of capturing the local structure. Since closed-form solutions for the proposed method are not easily obtained, we devise an iterative optimization algorithm for its resolution. We also analyze the computational complexity and convergence of DSL_AGR. Extensive experiments on real-world datasets demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.

Discriminative sparse subspace learning with manifold regularization

Robust descriptor-based subspace learning with complex data is an active topic in pattern analysis and machine intelligence. A few researches concentrate the optimal design on feature representation and metric learning. However, traditionally used features of single-type, e.g., image gradient orientations (IGOs), are deficient to characterize the complete variations in robust and discriminant subspace learning. Meanwhile, discontinuity in edge alignment and feature match are not been carefully treated in the literature. In this paper, local order constrained IGOs are exploited to generate robust features. As the difference-based filters explicitly consider the local contrasts within neighboring pixel points, the proposed features enhance the local textures and the order-based coding ability, thus discover intrinsic structure of facial images further. The multimodal features are automatically fused in the most discriminant subspace. The utilization of adaptive interaction function suppresses outliers in each dimension for robust similarity measurement and discriminant analysis. The sparsity-driven regression model is modified to adapt the classification issue of the compact feature representation. Extensive experiments are conducted by using some benchmark face data sets, e.g., of controlled and uncontrolled environments, to evaluate our new algorithm.

Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in the original space, and employ the maximum mean discrepancy as the distance measure to compute the distances between the sample means of the different distributions. Then the distance measure is integrated into Fisher criterion to learn a discriminative common feature subspace. The aim is to map the images with different ages to the common subspace, and to construct new feature representation which is robust to age variations and discriminative to different subjects. To evaluate the performance of the proposed method on cross-age face recognition, the authors construct extensive experiments on CACD and FG-Net databases. Experimental results show that the proposed method outperforms other subspace based methods and state-of-art cross-age face recognition methods.

Recently, cross-view feature learning has been a hot topic in machine learning due to the wide applications of multiview data. Nevertheless, the distribution discrepancy between cross-views leads to the fact that instances of the different views from same class are farther than those within the same view but from different classes. To address this problem, in this paper, we develop a novel cross-view discriminative feature subspace learning method inspired by layered visual perception from human. Firstly, the proposed method utilizes a separable low-rank self-representation model to disentangle the class and view structure layers, respectively. Secondly, a local alignment is constructed with two designed graphs to guide the subspace decomposition in a pairwise way. Finally, the global discriminative constraint on distribution center in each view is designed for further alignment improvement. Extensive cross-view classification experiments on several public datasets prove that our proposed method is more effective than other existing feature learning methods.

In the past few years, face attributes have attracted much attention. In this paper, for the first time we combine the discriminant subspace learning technique with the idea of pattern reconstruction to build a face attribute classification framework. For the attribute considered, the framework firstly learns an attribute subspace by using a discriminant subspace learning method, which also has the capability of pattern reconstruction. The framework then reconstructs the attribute state of input query image with the learned subspace, and classifies face attribute based on minimum reconstruction error. By repeatedly using the classification framework for different attributes, we can achieve multiple classification results output. According to the output, we select matching objects for each given query image based on generalized hamming distance to realize face recognition. The proposed attribute classification framework and face recognition approach are validated on the public AR and Weizmann face databases. Experimental results demonstrate their effectiveness as compared with several related methods.

Adaptive weighted nonnegative low-rank representation

Discriminative codebook learning for Web image search

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

Visual attributes are high-level semantic descriptions of visual data that are close to the human language. They have been used intensively in various applications such as image classification, active learning, and interactive search. However, the usage of attributes in subspace learning (or dimensionality reduction) has not been considered yet. In this work, we propose to utilize relative attributes as semantic cues in subspace learning. To this end, we employ Non-negative Matrix Factorization (NMF) constrained by embedded relative attributes to learn a subspace representation of image content. Experiments conducted on two datasets show the efficiency of attributes in discriminative subspace learning.

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from incomplete views to improve clustering performance. Most existing IMC methods try to fill the incomplete views or directly learn a common representation based on matrix factorization or subspace learning. The former may introduce useless even noisy information especially for data with a large missing ratio. The latter relies on the initialization and ignores the geometric structure of data. To address these issues, we propose a novel Joint Partition and Graph (JPG) learning method for IMC. Specifically, JPG jointly constructs local incomplete graph matrices, generates incomplete base partition matrices, stretches them to produce a unified partition matrix, and employs it to learn a consensus graph matrix. By this means, we transform incomplete multi-view data into a unified partition space and obtain the consensus graph in a mutual reinforcement manner. Moreover, a partition fusion strategy can allocate a large weight to the stretched base partition that is close to the unified matrix. The objective function is optimized in an alternating optimization fashion. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of JPG than the state-of-the-art baselines

Semi-supervised multi-view feature selection with adaptive similarity fusion and learning