Double adjacency graphs-based discriminant neighborhood embedding

Ding, Chuntao; Zhang, Li

doi:10.1016/j.patcog.2014.08.025

Cited by 42 publications

(21 citation statements)

References 12 publications

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“…Exploring new graph features is a persistent research focus in the graph data mining community, and many feature extraction methods have been proposed [13], [14]. Existing graph classification approaches can be roughly categorized into two groups: 1) distance based methods that include a pairwise similarity measure between two graphs, such as graph kernel [23], graph embedding [24], [25], [26], [27], graph matching [28], [29], and transformation [30], [31], [32]; and 2) subgraph feature based methods that identify significant subgraphs as signatures for one particular class. For example, the work in [33] proposed to the extraction of subgraph structural information for classification.…”

Section: Related Workmentioning

confidence: 99%

Time-Variant Graph Classification

Wang

Zhu

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet -a class of discriminative features designed for vector-based temporal data classification. To discover graphshapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.

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Section: Related Workmentioning

confidence: 99%

Time-Variant Graph Classification

Wang

Zhu

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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“…In this section we emphatically introduce three classical algorithms, MFA [ 10 ], LDNE [ 12 ] and DAG-DNE [ 13 ], which are related to our research point.…”

Section: Backgroudmentioning

confidence: 99%

“…Supervised algorithms conduct datasets with labels that aim to present better performance and low complexity. Linear discriminant analysis (LDA), local discriminant embedding (LDE) [ 7 ], discriminant sparse neighborhood preserving embedding (DSNPE) [ 8 ], regularized coplanar discriminant analysis (RCDA) [ 9 ], marginal Fisher analysis (MFA) [ 3 , 5 , 10 ], discriminant neighborhood embedding (DNE) [ 11 ], locality-based discriminant neighborhood embedding(LDNE) [ 12 ], and double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE) [ 13 ] are typical supervised algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Discriminant Analysis

Ding

et al. 2018

Sensors

Self Cite

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The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

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“…Then it finds an optimal embedding such that the neighborhood structure can be preserved in the subspace. Recently, many discriminative versions of NPE have been proposed, such as neighbourhood preserving discriminant embedding (NPDE) [21] and double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE) [22]. Different from NPE, where the nearest neighbors are manually choosen, SP-P [18,19] automatically constructs a graph.…”

Section: Introductionmentioning

confidence: 99%

Regularized coplanar discriminant analysis for dimensionality reduction

Huang

Dai

Ren

2017

Pattern Recognition

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The dimensionality reduction methods based on linear embedding, such as neighborhood preserving embedding (NPE), sparsity preserving projections (SPP) and collaborative representation based projections (CRP), try to preserve a certain kind of linear representation for each sample after projection. However, in the transformed low-dimensional space, the linear relationship between the samples may be changed, which can not make the linear representation-based classifiers, such as sparse representation-based classifier (SRC), to achieve higher recognition accuracy. In this paper, we propose a new linear dimensionality reduction algorithm, called Regularized Coplanar Discriminant Analysis (RCDA) to address this problem. It simultaneously seeks a linear projection matrix and some linear representation coefficients that make the samples from the same class coplanar and the samples from different classes not coplanar. The proposed regularization term

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Double adjacency graphs-based discriminant neighborhood embedding

Cited by 42 publications

References 12 publications

Time-Variant Graph Classification

Time-Variant Graph Classification

Hierarchical Discriminant Analysis

Regularized coplanar discriminant analysis for dimensionality reduction

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