Local Interclass Research Articles

A novel subdomain adaptive intelligent fault diagnosis method based on multiscale adaptive residual networks

Traditional fault diagnosis methods may not capture key information during feature extraction due to the large distribution difference under different working conditions, which can result in poor accuracy of the diagnostic model. To address this issue, a subdomain adaptive bearing fault identification method guided by the local maximal mean difference (LMMD) under the multiscale adaptive residual network is proposed in this paper. The bearing vibration signals are preprocessed by wavelet convolution and wide convolution to generate initial features. Then, the multi-scale adaptive residual network is used to adjust the feature weights of different scales and extract richer feature information. To reduce the intra-class distribution difference, the LMMD is employed. Additionally, local interclass maximum mean difference (LIMMD) is used to increase the inter-class difference, preventing misclassification of samples from different classes due to their close proximity and achieving sub-domain distribution alignment. The fault diagnosis performance of the domain distance metric model guided by LMMD and LIMMD under the multiscale adaptive residual network is verified through two different bearing model validation experiments.

Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image

Dimensionality reduction (DR) technology plays an important role in hyperspectral image (HSI) classification. However, many existing DR algorithms ignore the complex intrinsic structure in spatial domain and spectral domain of HSI. To address this issue, we put forward a spatial-spectral local discriminant projection (SSLDP) method based on the manifold learning theory and spatial consistency in HSI. In SSLDP, hyperspectral pixels are reconstructed by minimizing the weighted reconstruction errors to preserve the local geometric structure. Then, two weighted scatter matrices are designed to maintain the neighborhood structure in spatial domain and two reconstruction graphs are constructed to discover the local discriminant relationship in spectral domain. Finally, an objective function is designed for obtaining an optimal projection by compacting the spatial-spectral local intraclass points while separating the spatial-spectral local interclass points. The experiments performed on some real hyperspectral images, including the Indian Pines, PaviaU and Washington DC, demonstrate that the presented SSLDP algorithm is significantly superior to some state-of-the-art DR algorithms.

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.

A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis

Localgraph based discriminant analysis (DA) algorithms recently have attracted increasing attention to mitigate the limitations ofglobal(graph) DA algorithms. However, there are few particular concerns on the following important issues: whether the local construction is better than the global one for intraclass and interclass graphs, which (intraclass or interclass) graph should locally or globally be constructed? and, further how they should be effectively jointed for good discriminant performances. In this paper, pursuing our previous studies on the graph construction and DA, we firstly address the issues involved above, and then by jointly utilizing both the globality and the locality, we develop, respectively, a Globally marginal and Locally compact Discriminant Analysis (GmLcDA) algorithm based on so-introduced global interclass and local intraclass graphs and a Locally marginal and Globally compact Discriminant Analysis (LmGcDA) based on so-introduced local interclass and global intraclass graphs, the purpose of which is not to show how novel the algorithms are but to illustrate the analyses in theory. Further, by comprehensively comparing the Locally marginal and Locally compact DA (LmLcDA) based on locality alone, the Globally marginal and Globally compact Discriminant Analysis (GmGcDA) just based on globality alone, GmLcDA, and LmGcDA, we suggest that the joint of locally constructed intraclass and globally constructed interclass graphs is more discriminant.

Local sparse representation projections for face recognition

How to define the sparse affinity weight matrices is still an open problem in existing manifold learning algorithm. In this paper, we propose a novel supervised learning method called local sparse representation projections (LSRP) for linear dimensionality reduction. Differing from sparsity preserving projections (SPP) and the recent manifold learning methods such as locality preserving projections (LPP), LSRP introduces the local sparse representation information into the objective function. Although there are no labels used in the local sparse representation, it still can provide better measure coefficients and significant discriminant abilities. By combining the local interclass neighborhood relationships and sparse representation information, LSRP aims to preserve the local sparse reconstructive relationships of the data and simultaneously maximize the interclass separability. Comprehensive comparison and extensive experiments show that LSRP achieves higher recognition rates than principle component analysis, linear discriminant analysis and the state-of-the-art techniques such as LPP, SPP and maximum variance projections.

Multi-scale gist feature manifold for building recognition

Multi-scale gist (MS-gist) feature manifold for building recognition is presented in the paper. It is described as a two-stage model. In the first stage, we extract the multi-scale gist features that represent the structural information of the building images. Since the MS-gist features are extrinsically high dimensional and intrinsically low dimensional, in the second stage, an enhanced fuzzy local maximal marginal embedding (EFLMME) algorithm is proposed to project MS-gist feature manifold to low-dimensional subspace. EFLMME aims to preserve local intra-class geometry and maximize local interclass margin separability of MS-gist feature manifold of different classes at the same time. To evaluate the performance of our proposed model, experiments were carried out on the Sheffield buildings database, compared with the existing works: (a) the visual gist based building recognition model (VGBR) and (b) the hierarchical building recognition model (HBR). Moreover, EFLMME is evaluated on Sheffield buildings database compared with some linear dimensionality reduction methods. The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well.

Maximal local interclass embedding with application to face recognition

Dimensionality reduction of high dimensional data is involved in many problems in information processing. A new dimensionality reduction approach called maximal local interclass embedding (MLIE) is developed in this paper. MLIE can be viewed as a linear approach of a multimanifolds-based learning framework, in which the information of neighborhood is integrated with the local interclass relationships. In MLIE, the local interclass graph and the intrinsic graph are constructed to find a set of projections that maximize the local interclass scatter and the local intraclass compactness simultaneously. This characteristic makes MLIE more powerful than marginal Fisher analysis (MFA). MLIE maintains all the advantages of MFA. Moreover, the computational complexity of MLIE is less than that of MFA. The proposed algorithm is applied to face recognition. Experiments have been performed on the Yale, AR and ORL face image databases. The experimental results show that owing to the locally discriminating property, MLIE consistently outperforms up-to-date MFA, Smooth MFA, neighborhood preserving embedding and locality preserving projection in face recognition.

Discriminative learning by sparse representation for classification

Recently, sparsity preserving projections (SPP) algorithm has been proposed, which combines l 1-graph preserving the sparse reconstructive relationship of the data with the classical dimensionality reduction algorithm. However, when applied to classification problem, SPP only focuses on the sparse structure but ignores the label information of samples. To enhance the classification performance, a new algorithm termed discriminative learning by sparse representation projections or DLSP for short is proposed in this paper. DLSP algorithm incorporates the merits of both local interclass geometrical structure and sparsity property. That makes it possess the advantages of the sparse reconstruction, and more importantly, it has better capacity of discrimination, especially when the size of the training set is small. Extensive experimental results on serval publicly available data sets show the feasibility and effectiveness of the proposed algorithm.

Improving the discriminant ability of local margin based learning method by incorporating the global between-class separability criterion

Many applications in machine learning and computer vision come down to feature representation and reduction. Manifold learning seeks the intrinsic low-dimensional manifold structure hidden in the high-dimensional data. In the past few years, many local discriminant analysis methods have been proposed to exploit the discriminative submanifold structure by extending the manifold learning idea to supervised ones. Particularly, marginal Fisher analysis (MFA) finds the local interclass margin for feature extraction and classification. However, since the limited data pairs are employed to determine the discriminative margin, such method usually suffers from the maladjusted learning as we introduced in this paper. To improve the discriminant ability of MFA, we incorporate the marginal Fisher idea with the global between-class separability criterion (BCSC), and propose a novel supervised learning method, called local and global margin projections (LGMP), where the maladjusted learning problem can be alleviated. Experimental evaluation shows that the proposed LGMP outperforms the original MFA.

Nonparametric Feature Selection Method Based on Local Interclass Structure

A nonparametric feature selection method which can be applicable to pattern recognition problems based on mixed features is presented. In the pattern space, each pattern class is represented by multiple subregions according to local interclass structure. Then in each of the subregions, feature selection is performed in a simple nonparametric way. Our feature selection method can select a feature subset based on higher order discriminating information. Some basic properties of our approach are presented theoretically and experimentally.