Face recognition with manifold-based kernel discriminant analysis

Araabi, Babak Nadjar; Gharibshah, Zhabiz

doi:10.1109/ijcnn.2012.6252677

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Network representation learning has gained a lot of attentions recently [5]. The classic dimension reduction methods like LLE [6], ISOMAP [7], Laplacian eigenmaps [8] and their extensions [9], [10] adopted various versions of linear and non-linear matrix factorization based techniques. Although the performance of these methods are shown successful for relatively small networks, but applying matrix decomposition is not scalable for large networks.…”

Section: Related Workmentioning

confidence: 99%

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TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

Gharibshah,

Zhu

2020

Preprint

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In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. We argue that tripartite networks are common in realworld applications, and the essential challenge of the representation learning is the heterogeneous relations between various node types and links in the network. To tackle the challenge, we develop a tripartite heterogeneous network embedding called TriNE. The method considers unique user-item-tag tripartite relationships, to build an objective function to model explicit relationships between nodes (observed links), and also capture implicit relationships between tripartite nodes (unobserved links across tripartite node sets). The method organizes metapathguided random walks to create heterogeneous neighborhood for all node types in the network. This information is then utilized to train a heterogeneous skip-gram model based on a joint optimization. Experiments on real-world tripartite networks validate the performance of TriNE for the online user response prediction using embedding node features.

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Section: Related Workmentioning

confidence: 99%

“…w ik e ik ∈E 3 w ik (9) In this case, w ij and w jk are the weights of edge e ij and e jk between nodes in V 1 and V 2 or V 2 and V 3 parties respectively.…”

Section: Explicit Relationship Modelingmentioning

confidence: 99%

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

Gharibshah,

Zhu

2020

Preprint

Self Cite

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“…Obtained results are listed in Table 3 where the highest accuracy were achieved compared to recent techniques. In MKDA [18] the authors need pre-processing, down sampling step to achieve the mentioned results while the proposed algorithm did not need any pre-processing steps. LOO protocol achieved 100 % recognition accuracy.…”

Section: Umist Data Setmentioning

confidence: 99%

Real time vision/sensor based features processing for efficient HCI employing canonical correlation analysis

El-Shazly

Abdelwahab

Shimada

et al. 2016

J Reliable Intell Environ

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In this paper, a global algorithm for facial and gesture recognition is presented. The algorithm basically consists of three modules: features sensing and processing, dominant features selection and finally features matching. Depending on the type of data used (vision or sensor based), the proposed algorithm exploits multiple features employing 2DPCA that efficiently compact features' descriptors maintain the spatial and temporal alignment of features' components. Canonical Correlation Analysis (CCA) is employed to fuse different features from different descriptors or different performers. CCA also transforms training and testing features sets into new space where similar pairs become highly correlated pairs. Different experiments were conducted using well known data sets in addition to our newly collected data sets to verify the efficiency of the proposed algorithm. Excellent recognition accuracy, and fast performance are factors that promotes the proposed algorithm for real time implementation.

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TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

Gharibshah

Zhu

2020

2020 IEEE International Conference on Knowledge Graph (ICKG)

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Face recognition with manifold-based kernel discriminant analysis

Cited by 4 publications

References 31 publications

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

Real time vision/sensor based features processing for efficient HCI employing canonical correlation analysis

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

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