A Review of Deep Machine Learning

Benuwa, Ben‐Bright; Zhan, Yong Zhao; Ghansah, Benjamin; Wornyo, Dickson Keddy; Kataka, Frank Banaseka

doi:10.4028/www.scientific.net/jera.24.124

Cited by 69 publications

(28 citation statements)

References 55 publications

(81 reference statements)

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“…Based on the experimental results, it could be seen that the proposed SLSDDL algorithm for video semantic analysis is more effective and outperforms the other state-of-the-art approaches such as LLC, FDDL, LSDL, and KSVD. Despite superior results demonstrated by the proposed SLSDDL on the TRECVID, YouTube, and OV video datasets, there is still the need to improve the execution time and further improve the power of discrimination; hence we plan on using deep learning discussed in [36] for the extraction of the video features and also introducing kernel into the structure in our future work.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Benuwa

Zhan

Ghansah

et al. 2018

Mathematical Problems in Engineering

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Dictionary learning (DL) and sparse representation (SR) based classifiers have greatly impacted the classification performance and have had good recognition rate on image data. In video semantic analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called sparsity based locality-sensitive discriminative dictionary learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of locality-sensitive dictionary learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Benuwa

Zhan

Ghansah

et al. 2018

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the experimental results, it could be seen that, the proposed SLSDDL algorithm for video semantic analysis is more effective and outperforms the other state-of-the-art approaches such as LLC, FDDL, LSDL and KSVD. Despite a superior results demonstrated by the proposed SLSDDL on the TRECVID, YouTube and OV video datasets, there is still the need to improve on the execution time and further improve the power of discrimination hence we plan use deep learning discussed in [36] for the extraction of the video features and also introduce kernel into the structure in our future work.…”

Section: Parameter Selectionmentioning

confidence: 99%

Sparsity Based Locality –Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Benuwa

Zhan

Ghansah

et al. 2018

Preprint

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“…17 In detail, multiple nonlinear transformation is used where the input of the following layer is the outputs from the previous layer. Deep learning applies multiple and complex structured layers of neuron network derived from large-scale data set for pattern analysis and classification.…”

Section: Machine Learningmentioning

confidence: 99%

Comprehensive analysis of network traffic data

Miao

Ruan

Pan

et al. 2017

Concurrency and Computation

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With the large volume of network traffic flow, it is necessary to preprocess raw data before classification to gain the accurate results speedily. Feature selection is an essential approach in preprocessing phase. The principal component analysis (PCA) is recognized as an effective and efficient method. In this paper, we classify network traffic flows by using the PCA technique together with 6 machine learning algorithms-Naive Bayes, decision tree, 1-nearest neighbor, random forest, support vector machine, and H 2 O. We analyzed the impact of PCA on the classification results by applying each algorithm with and without PCA onto the data set. Experiments were set out by varying the size of input data sets, and the performances were measured from 2 aspects, including average overall accuracy and F-measure. The computational time was also considered in analyzing the performance. Our results showed that random forest and 1-nearest neighbor were the top 2 algorithms among all the 6 regarding the 2 metrics mentioned above. Then we continued the study of PCA impact on per class level with these 2 algorithms as examples. And the positive correlation between overall impact and the number of class with significant impact was revealed.Lastly, the visualization was used in exploring the reasons of the impacts caused by PCA. Two factors are considered in PCA's impact on per class level: benefit for classes grouped by PCA and mislabeled error interfered by nearby groups.

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A Review of Deep Machine Learning

Cited by 69 publications

References 55 publications

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Sparsity Based Locality –Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Comprehensive analysis of network traffic data

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A Review of Deep Machine Learning

Cited by 69 publications

References 55 publications

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Sparsity Based Locality &ndash;Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Comprehensive analysis of network traffic data

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About

Sparsity Based Locality –Sensitive Discriminative Dictionary Learning for Video Semantic Analysis