Convolutional Neural Network Based on Principal Component Analysis Initialization for Image Classification

Ren, Xuhua; Guo, Haonan; He, Guan-Chen; Xu, Xu; Di, Chong; Li, Shenghong

doi:10.1109/dsc.2016.18

Cited by 22 publications

(12 citation statements)

References 4 publications

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“…Its goal was to increase the performance of deep learning in extracting features and artificial intelligence in classifying data points into the correct groups. In a deep learning network, such as CNN, the gradient diffusion problem occurs [ 39 , 40 ], and many of the filters in the layer are highly correlated thus making it possible to detect the same feature [ 41 ], and making insignificant contributions to the classification accuracy performance. To alleviate these problems by initializing the weights of convolution kernels, PCA is employed to the unsupervised extraction of input image eigenvectors [ 39 , 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

“…In a deep learning network, such as CNN, the gradient diffusion problem occurs [ 39 , 40 ], and many of the filters in the layer are highly correlated thus making it possible to detect the same feature [ 41 ], and making insignificant contributions to the classification accuracy performance. To alleviate these problems by initializing the weights of convolution kernels, PCA is employed to the unsupervised extraction of input image eigenvectors [ 39 , 41 , 42 ]. PCA can also improve the classification performance (accuracy, sensitivity, specificity, and AUC value; see Section 3 Experimental Results).…”

Section: Methodsmentioning

confidence: 99%

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Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Setiawan

Lin

2021

Brain Sciences

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A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Setiawan

Lin

2021

Brain Sciences

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“…One of the important tools in literature to control the power supply is the Arduino micro-controller which has its usage in a wide range of applications. A system to quantify the energy of the given load and plan appropriate energy conservation policies was also designed [27]. Another application was developed for smart home energy management systems (SHEMS) [28] to enable/disable the power supply when a human is detected/undetected respectively.…”

Section: Related Workmentioning

confidence: 99%

Real-time human detection for electricity conservation using pruned-SSD and arduino

Ushasukhanya

Jothilakshmi

2021

IJECE

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Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

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“…LeNet-5 is a classic CNN architecture for performing image classification, such as handwritten or machine printed character recognition, and multi-object detection [12,31,32].…”

Section: Lenet-5mentioning

confidence: 99%

“…The sub-sampling layer serves to reduce the characteristics of the inputs and the computational complexity. Finally, the FC layer, that is a layer of a normal neural network where each pixel is considered as a neuron, functions to calculate the scores of each of the classes [12,31]. FC layers have a loss function as a SVM or softmax classifier [4].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Vibration analysis in bearings for failure prevention using CNN

Pinedo-Sanchez

Mercado-Ravell

Carballo-Monsivais

2020

J Braz. Soc. Mech. Sci. Eng.

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Convolutional Neural Network Based on Principal Component Analysis Initialization for Image Classification

Cited by 22 publications

References 4 publications

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Real-time human detection for electricity conservation using pruned-SSD and arduino

Vibration analysis in bearings for failure prevention using CNN

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