Least Square Incremental Linear Discriminant Analysis

Liu, Liping; Jiang, Yuan; Zhou, Zhi‐Hua

doi:10.1109/icdm.2009.78

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…Seng et al [24], in their groundbreaking work, had used Bi-Directional Principle Component analysis (BDPCA) [26] and Least-Square Linear Discriminant Analysis (LSLDA) [27] for dimensionality reduction and class discrimination. In their study, these two models were cascaded with the output of BDPCA being used as the input of LSLDA [27]. The extracted features are then forwarded to Convolutional Neural network.…”

Section: Zhang Et Al In Their Workmentioning

confidence: 99%

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

Sharma*¹,

Mansotra²

2019

IJRTE

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Classroom environment is a competent platform for the students to learn and improve their understanding of the subject. An instructor’s primary responsibility lies in managing the students in a way they feel interested and focused during the class. With the aid of automated systems based on artificial intelligence, an instructor can get feedback on the students’ attention span in the class by monitoring their emotions using learning algorithms that can prove to be effective to improve the teaching style of the instructor that can in turn have positive effects on the class. In this paper, we propose an LSTM recurrent neural network trained on an emotional corpus database to extract the speech features and convolutional neural networks trained on the FER2013 facial emotion recognition database were used to predict the speech and facial emotions of the students respectively, in real-time. The live video and audio sequence of the students captured is fed to the learned model to classify the emotions individually. Once the emotions such as anger, sadness, happiness, surprise, fear, disgust and neutral were identified, a decision-making mechanism was used to analyze the predicted emotions and choose the overall group emotion by virtue of the highest peak value achieved. This research approach has the potential to be deployed in video conferences, online classes etc. This implementation proposal should effectively improve the classification accuracy and the relatability of the detected student emotions and facilitate in the design of sophisticated automated learning systems that can be a valuable tool in evaluating both the students and the instructors. The adapted research methodologies and their results are discussed and found to perform suggestively better than the other research works used in the comparison

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Section: Zhang Et Al In Their Workmentioning

confidence: 99%

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

Sharma*¹,

Mansotra²

2019

IJRTE

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“…As for learning from data streams, some algorithms are naturally or can be easily extended to incremental version, including k-NN, naive Bayes classifier, binary linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and so on. In addition, the incremental/online versions of more sophisticated algorithms have been proposed in the literature, including but not limited to decision trees [46], random forests [41], [11] , multi-class LDA [34], [28], logistic regression [31], support vector machines [32], [5], and other kernel methods [29], [25]. Besides the base learning algorithms, the online versions of ensemble learning techniques, bagging and boosting, were also derived in [36] by approximating binominal distribution using a Poisson distribution.…”

Section: Related Workmentioning

confidence: 99%

Online Bagging and Boosting for Imbalanced Data Streams

Wang¹,

Pineau²

2016

IEEE Trans. Knowl. Data Eng.

153

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While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within this framework, two separately developed research areas are bridged together, and a batch of theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms are first proposed. Unlike other online cost-sensitive learning algorithms lacking theoretical analysis of asymptotic properties, the convergence of the proposed algorithms is guaranteed under certain conditions, and the experimental evidence with benchmark data sets also validates the effectiveness and efficiency of the proposed methods.

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“…Furthermore, many well-optimised algorithms [34] can be readily applied to large data. Finally, its adaptable solution supports efficient model update for incremental learning [28]. The new HER model casts re-id into such a regression problem, benefiting from all of its advantages in scalability.…”

Section: Related Workmentioning

confidence: 99%

“…However, this work is the first to formulate it for a verification setting as in re-id. For its incremental extension, our model HER + , differs significantly to [28] which only supports updates on a single sample without regularisation employed.…”

Section: Related Workmentioning

confidence: 99%

Highly Efficient Regression for Scalable Person Re-Identification

Wang

Gong

Xiang

2016

Procedings of the British Machine Vision Conference 2016

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Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making realtime active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment.

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Least Square Incremental Linear Discriminant Analysis

Cited by 32 publications

References 11 publications

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

Online Bagging and Boosting for Imbalanced Data Streams

Highly Efficient Regression for Scalable Person Re-Identification

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