An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm

Yang, Ping; Wang, Dan; Wei, Zhuojun; Du, Xiaolin; Li, Tong

doi:10.1109/access.2019.2922004

Cited by 33 publications

(8 citation statements)

References 37 publications

(35 reference statements)

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“…The [36] feature Map we get here is smaller than the size of the image. As we increase the value of stride, the size of the Feature Map decreases.…”

Section: Fig-11: Shifting Of Filter By One Columnmentioning

confidence: 98%

Machine learning based model to predict Human Emotions using facial features

Singhal¹

2022

IJSREM

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This paper focuses on Emotion Recognition using facial expressions. It aims to detect facial expressions accurately and efficiently. Here, we have used facial expressions as the criteria wherein the lips, eyebrow movement, eyes, etc. are the factors that help us detect various emotions. The purpose of this paper is to serve various fields where emotion recognition will be viable and can be helpful for better outcomes. Spectrums like video gaming, psychology, medical fields, etc. can use Emotion Recognition System in a very positive way. Convolutional Neural Network has developed to help in recognizing emotions through facial expressions and classify them into seven basic categories which are: happy, sad, neutral, surprise, fear, disgust, and angry. So, Convolutional Neural Network is implemented to extract relevant features of the input images and classify them into seven categories. For evaluating the proposed model, Facial Emotion Recognition 2013 dataset is used so that the model achieves the best accuracy rate. Keywords—Convolutional Neural Network (CNN), Facial Emotion Recognition 2013 (FER 2013), Extended Cohn-Kanade (CK+), Japanese Female Facial Expressions (JAFFE), Emotional Facial Action Coding System (EMFACs), Action Units (AUs).

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“…The [36] feature Map we get here is smaller than the size of the image. As we increase the value of stride, the size of the Feature Map decreases.…”

Section: Fig-11: Shifting Of Filter By One Columnmentioning

confidence: 98%

Machine learning based model to predict Human Emotions using facial features

Singhal¹

2022

IJSREM

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“…Accuracy and time involved in outlier detection was not focused. To address this aspect, a Neighbor Entropy Local Outlier Factor was presented in [18] that with the aid of self organizing feature map not only improved accuracy but also reduced the execution time to a greater extent. Moreover, semantic information was focused on [19] for outlier detection employing meta path based outlier detection.…”

Section: Related Workmentioning

confidence: 99%

“…It is measured in terms of percentage (%). Finally, Table IV lists the error rate obtained using the (18). Finally, Fig.…”

Section: Case Analysis Of Error Ratementioning

confidence: 99%

Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

Rajalakshmi¹,

Madhubala²

2022

IJACSA

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Outlier detection or simply the task of point detection that are noticeably distinct and different from data sample is a predominant issue in deep learning. When a framework is constructed, these distinctive points can later lead to model training and compromise accurate predictions. Owing to this reason, it is paramount to recognize and eliminate them before constructing any supervised model and this is frequently the initial step when dealing with a deep learning issue. Over the recent few years, different numbers of outlier detector algorithms have been designed that ensure satisfactory results. However, their main disadvantages remain in the time and space complexity and unsupervised nature. In this work, a clusteringbased outlier detection called, Random Projection Deep Extreme Learning-based Chebyshev Reflective Correlation (RPDEL-CRC) is proposed. First, Gaussian Random Projection-based Deep Extreme Learning-based Clustering model is designed. Here, by applying Gaussian Random Projection function to the Deep Extreme Learning obtains the relevant and robust clusters corresponding to the data points in a significant manner. Next, with the robust clusters, outlier detection time is said to be reduced to a greater extent. In addition, a novel Chebyshev Temporal and Reflective Correlation-based Outlier Detection model is proposed to detect outliers therefore achieving high outlier detection accuracy. The proposed approach is validated with the NIFTY-50 stock market dataset. The performance of the RPDEL-CRC method is evaluated by applying it to NIFTY-50 Stock Market dataset. Finally, we compare the results of the RPDEL-CRC method to the state-of-the-art outlier detection methods using outlier detection time, accuracy, error rate and false positive rate evaluation metrics.

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“…Amongst the class of local neighborhood-based outlier detection, the local outlier factor is an algorithm proposed by Hans-Peter Kriegel et al in 2000 for finding abnormal data points by calculating the local deviation (outliers) of a given data point with respect to its neighbors [12,18,19].…”

Section: Related Workmentioning

confidence: 99%

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

Rehman¹,

Muhammad²

2020

IJACSA

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In recent times, dimension size has posed more challenges as compared to data size. The serious concern of high dimensional data is the curse of dimensionality and has ultimately caught the attention of data miners. Anomaly detection based on local neighborhood like local outlier factor has been admitted as state of art approach but fails when operated on the high number of dimensions for the reason mentioned above. In this paper, we determine the effects of different distance functions on an unlabeled dataset while digging outliers through the density-based approach. Further, we also explore findings regarding runtime and outlier score when dimension size and number of nearest neighbor points (min_pts) are varied. This analytic research is also very appropriate and applicable in the domain of big data and data science as well.

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An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm

Cited by 33 publications

References 37 publications

Machine learning based model to predict Human Emotions using facial features

Machine learning based model to predict Human Emotions using facial features

Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

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