Locally connected graph embedding for semisupervised image classification

Dimensionality reduction via preserving local information

Electrochemical performance of manganese hexacyanoferrate cathode material in aqueous Zn-ion battery

Cartoon-Texture decomposition with patch-wise decorrelation

Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.

Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.

We propose a semisupervised sparse graph learning (SSGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SSGL method aims to build a semisupervised sparse graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised sparse graph, we connect labeled samples according to their label information and sparse representation, connect unlabeled samples by sparse representation which set unlabeled training samples set as dictionary. Moreover, by setting sparse connections between labeled sample and unlabeled samples, the label information can be well propagated from labeled samples to unlabeled samples. In this way, the similarity of data points can be well modelled in the spectral feature space, and the data distribution characteristics can be better expressed. Experimental results on real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.

Abstract. In this paper, we proposed a novel 3D deep learning model for object localization and object bounding boxes estimation. To increase the detection efficiency of small objects in the large scale scenes, the local neighbourhood geometric structure information of objects has been taken into the Edgeconv model, which can operate the original point clouds. We evaluated the 3D bounding box with high resolution in the RGB-D dataset and acquired stable effectiveness even under the sparse points and the strong occlusion. The experimental results indicate that our method achieved the higher mean average precision and better IOU of bounding boxes in SUN RGB-D dataset and KITTI benchmark.

Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher discrimination sparse learning based on graph embedding (GE-FDSL) scheme. First, we utilizes graph embedding framework to define intra-class compact matrix and inter-class separable matrix imposed on the coding coefficients of training samples to preserving the intra-class compactness and the inter-class separability for the training samples, which simultaneously consider the local manifold structure and label information of the coding coefficients. Then, a new Fisher discrimination criterion based on graph embedding is added to the object function of the sparse coding problem so that the coding coefficients have more discriminative power, where the dictionary atoms in the sparse coding model are associated with the class labels so that the reconstructed error is applied to classification. This method can learn a structured dictionary and sparse coefficients, and in the meantime, it will also keep the local manifold structure of the coding coefficients. So, they will be more discriminative. Experiments on many image databases show that the our algorithm has good classification and recognition performance.

Discriminative globality and locality preserving graph embedding for dimensionality reduction

Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys

Gene expression profiles have been used to predict cancer recurrence or other clinical outcomes of cancer patients. However, clinical information of cancer patients is often incomplete, which yields many unlabeled samples that cannot be used in supervised learning. In this is paper, we develop a novel semi-supervised leaning (SSL) method that uses both labeled and unlabeled patient samples to predict cancer recurrence. Our new SSL algorithm employs a sparse representation approach where a labeled sample is represented as a combination of a small number of properly chosen unlabeled samples. Experiments with a set of gene expression data from patients with colorectal cancer(CRC) demonstrate that our SSL algorithm can improve prediction accuracy compared to other two SSL methods including TSVM and T3VM, and the traditional support vector machine.

This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further we extract local steering kernel (LSK) based feature to preserve geometric structure and texture sharpness in the target region. The advantage of non local self similarity as redundancy of similar patches in natural images is introduced to find the candidate patches from the whole source region. Based on these local and non local prior information we propose a sparse representation framework for image inpainting. Our proposed method is tested on wide range of natural images. The experimental results show the superiority of the proposed method compared to some of the previous approaches.

Depth estimation from a single image is very challenging due to the inherent ambiguity of mapping a color image to a depth map. Previous work tackles this problem by exploiting various levels of features with multi-scale deep convolutional neural networks. However, most of the local geometric structure related monocular depth cues are lost when being propagated through convolutional neural network. Moreover, the error of depth cues related to local geometric structures is not considered in the loss function. In this work, we propose the GeoCueDepth convolutional neural network to exploit local geometric structure cues and propose a training loss that takes the geometric error into consideration, which significantly improve the performance of depth prediction in both accuracy and sharpness. Experiments show that the proposed method achieves 0.122 average relative error and 0.078 square relative error on the NYU Depth v2 data set, which outperforms state-of-the-art monocular depth estimation approaches.

Spatio-temporal scale information fusion of Functional Near-Infrared Spectroscopy signal for depression detection

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.