Collaborative Coefficients Research Articles

A study of Authorship Pattern and Collaborative Measures of Pt. Ravishankar Shukla University, Raipur Chhattisgarh

The study highlights the Authorship pattern and collaborative measures of the 1692 Scholarly publications published in the Web of Science database for 21 years from 1989 to 2020. The study reveals the various significant aspects like the Collaborative index, collaborative coefficients, the correlation between authors and articles, and the degree of collaboration. There is a highly significant correlation found between articles and the authors. The highest collaborative index (3.32) and collaborative coefficient (0.64) were recorded in the year 2000. 1644 publications were co-authorship index while 48 publications single author index out of 1692 publications.

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image (HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensionality reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed. Finally, the future development of this research field is prospected.

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.

Semisupervised collaborative representation graph embedding for hyperspectral imagery

Graph embedding (GE) frameworks are used for extracting the discriminative features of hyperspectral images (HSIs). However, it is difficult to select a proper neighborhood size for graph construction. To overcome this difficulty, a semisupervised feature extraction (FE) method, called semisupervised collaborative representation graph embedding (SCRGE), is proposed. The proposed algorithm utilizes collaborative representation (CR) to obtain the collaborative coefficients of labeled and unlabeled samples. Then, a semisupervised graph is constructed using the collaborative coefficients of the labeled samples within the same class and the collaborative coefficients of the unlabeled samples, and an interclass graph is constructed using the collaborative coefficients of the labeled samples in different classes. Finally, a projection matrix for FE is obtained by embedding these graphs into a low-dimensional space. SCRGE not only inherits the merits of CR to reveal the collaborative reconstructive properties of data but also enhances intraclass compactness and interclass separability to improve the discriminating power for classification. Experimental results on three real HSIs datasets demonstrate that SCRGE outperforms other state-of-the-art FE methods in terms of classification accuracy.

Unsupervised Change Detection Based on a Unified Framework for Weighted Collaborative Representation With RDDL and Fuzzy Clustering

In this paper, we propose a novel unsupervised change detection method of remote sensing (RS) images based on a unified framework for weighted collaborative representation (WCR) with robust deep dictionary learning (RDDL) and fuzzy clustering. Specifically, WCR is employed to collaboratively represent neighborhood features with lower computational complexity, for which the RDDL model is built to learn more effective and representative overcomplete dictionary and enhance the robustness against the noise and outliers. Meanwhile, in order to make the resulting collaborative coefficients more beneficial for clustering, the unified framework for WCR with RDDL and fuzzy clustering is designed. By doing so, our framework not only precludes the utilization of third-party clustering algorithm, but also achieves better detection performance. Subsequently, the spatial constraint is enforced on the membership matrix to yield the updated one for further improving the accuracy of change detection. Finally, a binary change mask (CM) is achieved by assigning the pixels into the changed and unchanged classes. Experiments are performed on five pairs of RS images, and experimental results demonstrate the effectiveness of the proposed method.

Two-phase probabilistic collaborative representation-based classification

The probabilistic collaborative representation-based classification (PCRC), as a novel extension of collaborative representation-based classification (CRC), is a promising method in pattern recognition. In this article, we adopt the coarse to fine representation to propose two-phase probabilistic collaborative representation based-classification (TPCRC) to enhance the power of pattern discrimination in PCRC. In TPCRC, the first phase is to utilize probabilistic collaborative representation to coarsely choose the nearest representative samples, and the second phase is to use the chosen nearest samples to finely represent and classify each testing sample. In order to employ the locality of data to further improve classification performance of PCRC, we also propose two-phase weighted probabilistic collaborative representation based-classification (TWPCRC). In the fine representation of TWPCRC, the probabilistic collaborative coefficients are weighted by the local distance similarities between each testing sample and all the training samples. The proposed methods are verified by the comparative experiments on three public image data sets. Experimental results show that the proposed methods outperform the state-of-the-art collaborative representation-based classification methods.

A two-stage learning approach to face recognition

This paper introduces the Collaborative Representation (CR) techniques to small sample size conditions, and propose a Two-Stage learning approach to face recognition based on Collaborative Representation (TSCR). Based on the assumption that the same class samples should lie in the same subspace, we first use the unlabeled samples as dictionary atoms to construct each labeled sample, and obtain the collaborative coefficients by CR. The unlabeled sample with the largest collaborative coefficient is assigned the same class label as the reconstructed labeled sample, and is added to the labeled data set. This process is repeated until about half of the unlabeled samples are labeled and added to the labeled dataset. After that, we employ the original CR approach to classify the left unlabeled samples based on the newly labeled dataset. Experimental results demonstrate that the proposed TSCR is effective on face recognition.

Robust visual ℓ2-regularized least squares tracker with Bayes classifier and coding error

Visual object tracking is a challenging task due to the existence of significant illumination change, appearance variation, and occlusion. We propose a structured collaborative representation-based visual tracking algorithm by using both the generative appearance model and the discriminative model. First, a structured collaborative representation model based on l 2 -regularized least squares is used to exploit both holistic and local information of the target. In the structured collaborative representation framework, a new over-complete dictionary containing both target and background templates is proposed to enhance the robustness of the tracking algorithm. Second, the tracking task is treated as a binary classification problem, and a Bayes classifier is trained online by the use of structured collaborative representation coefficients of positive and negative samples. Furthermore, a residual error score is constructed to improve the detective ability of the tracker. Finally, target templates are updated by combining incremental subspace learning and collaborative representation together, and background templates are subsequently updated by samples around latest results. Compared with four state-of-the-art tracking algorithms in 12 challenging video sequences, the proposed tracking algorithm demonstrates a better performance overall in terms of experimental results.

A Collaborative Neighbor Representation Based Face Recognition Algorithm

We propose a new collaborative neighbor representation algorithm for face recognition based on a revised regularized reconstruction error (RRRE), called the two-phase collaborative neighbor representation algorithm (TCNR). Specifically, the RRRE is the division of l2-norm of reconstruction error of each class into a linear combination of l2-norm of reconstruction coefficients of each class, which can be used to increase the discrimination information for classification. The algorithm is as follows: in the first phase, the test sample is represented as a linear combination of all the training samples by incorporating the neighbor information into the objective function. In the second phase, we use thekclasses to represent the test sample and calculate the collaborative neighbor representation coefficients. TCNR not only can preserve locality and similarity information of sparse coding but also can eliminate the side effect on the classification decision of the class that is far from the test sample. Moreover, the rationale and alternative scheme of TCNR are given. The experimental results show that TCNR algorithm achieves better performance than seven previous algorithms.