A novel semi-supervised learning for face recognition

Gao, Quanxue; Huang, Yunfang; Gao, Xinbo; Shen, Weiguo; Zhang, Hailin

doi:10.1016/j.neucom.2014.11.018

Cited by 31 publications

(9 citation statements)

References 36 publications

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“…They may come in the form of unsupervised learning [15]- [17] clustering without target specification and supervised learning that is used for training to model in estimation before new data estimation. Semi-supervised learning [5], [13], [18]- [20] is another type involved in function estimation on labeled and unlabeled data, falling between unsupervised learning and supervised learning, and Ensemble Learning [21]- [23] which uses many classification models to vote on an estimation. However, even if the ensemble has to build multiple models for high-quality voting, it may not be suitable for intrusion detection on an always-available network.…”

Section: Relate Studiesmentioning

confidence: 99%

“…Many previous studies had applied LDA with other machine learning to enhance IDS together with newer intrusion datasets. Likewise, some studies developed and improved LDA algorithms [11], [13] to make the application more effective and more suitable such as Incremental Linear Discriminant Analysis (ILDA) [14] This method used incremental loading for processing. Similar to LDA generalization that models specific processes when finishing without reusing, it is considered as a method suitable for modern IDS.…”

Section: Introductionmentioning

confidence: 99%

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A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

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A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.

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Section: Relate Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

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“…A semisupervised approach has been proposed by Gao. 24 To solve the overfitting problem that may impair data's intrinsic structure 25,26 existing in Laplacian embedding (LE) algorithm, 27 they built an objective function to learn the intrinsic structure concluding the local topology and geometrical properties of data, and then incorporate this objective function into LDA's objective function. To be specific, they constructed an adjacency graph to overcome the shortcoming of LE and built a semisupervised approach called stable semisupervised discriminant learning (SSDL).…”

Section: Related Workmentioning

confidence: 99%

Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Tan

2017

Concurrency and Computation

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Summary In the field of machine learning, feature extraction is one of the most important preprocessing in data classification for its effectiveness, and now it has attracted much extensive attention for large‐scale data stream preprocessing step, especially in the era of big data. Motivated by the advantages of unsupervised and supervised feature extraction, which are two desirable and promising characteristics for dimension reduction, a new semisupervised local preserving embedding algorithm based on maximum margin criterion (SLPE/MMC) is proposed in this paper. First, the objective functions of maximum margin criterion (MMC) and neighborhood preserving embedding (NPE) are combined to get the first objective function of SLPE/MMC. Then, in order to overcome the out‐of‐sample problem, a linear transformation is introduced to construct the second objective function. At last, the whole optimal objective function is constructed by combing the two objective functions together. The proposed algorithm has effectively taken advantage of the sample's supervised information and keeps the geometry structure and the class discrimination information of the manifold. Experiments on face datasets Yale, CMU PIE, and AR datasets are performed to evaluate the classification accuracy of SLPE/MMC. The experimental results and time complexity comparisons have demonstrated the effectiveness of the proposed method.

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“…In [22], a function is introduced to learn essential structure representation that well identifies both similarity and diversity of data for face recognition. It combines the structure representation and linear discriminant analysis to make a semi-supervised approach, called stable semi-supervised discriminant learning (SSDL).…”

Section: Related Workmentioning

confidence: 99%

Online semi-supervised multi-channel time series classifier based on growing neural gas

Nooralishahi

Seera

Loo

2016

Neural Comput & Applic

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Challenges in time series classification has attracted attention in the past decade. Although large amounts of labeled data are assumed to be available, in reality, labeled data might be scarce to find in many domains. In this paper, we propose an online semi-supervised multi-channel classifier for time series based on growing neural gas (GNG) learning scheme. The method is able to handle multi-channel time series with variation in dimensions and it introduces a label prediction strategy to minimize misclassification. It measures the similarity of input instance and learned templates using weighted multichannel dynamic time warping technique and learns the topology of input data space specified for each class using the GNG learning algorithm. Comprehensive evaluation is conducted using various datasets, such as gesture recognition, human activity recognition, and human daily-life activity recognition. Experimental results demonstrate good classification results, with indication that the proposed approach requires only a handful of labeled instances to construct an accurate classification model.

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A novel semi-supervised learning for face recognition

Cited by 31 publications

References 36 publications

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Online semi-supervised multi-channel time series classifier based on growing neural gas

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