Deep learning for location prediction on noisy trajectories

Kandhare, Pravinkumar G.; Nakhmani, Arie; Sirakov, Nikolay

doi:10.1007/s10044-022-01095-y

Cited by 4 publications

(6 citation statements)

References 34 publications

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“…Note that the image is a 2D signal and can be presented in the form of a 1D signal or as a sequence. Despite this, we could roughly divide NNs into those which are designed to work with images [6,8] and those designed to work with signals and sequences of numbers [28,29]. A well-established representative of the first kind is the convolutional NN (CNN) [6,8], while examples of the second kind include long short-term memory (LSTM) NNs [28,29].…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…Despite this, we could roughly divide NNs into those which are designed to work with images [6,8] and those designed to work with signals and sequences of numbers [28,29]. A well-established representative of the first kind is the convolutional NN (CNN) [6,8], while examples of the second kind include long short-term memory (LSTM) NNs [28,29]. The main hyperparameters of an NN consist of the architecture, input layers, hidden layers, output layers, activation functions, loss function, optimization method and training process.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…The architecture of an NN typically consists of multiple interconnected layers, each containing a set of neurons or nodes. The arrangement and number of layers, as well as the number of neurons in each layer, vary depending on the specific problem and desired model complexity [8,12,28,29]. The NN model we use consists of two hidden layers and an output layer.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

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Training Data Augmentation with Data Distilled by Principal Component Analysis

Sirakov,

Shahnewaz,

Nakhmani

2024

Electronics

Self Cite

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This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. Hence, the novelty of this study is the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: a neural network, logistic regression and support vector machine with linear and polynomial kernels. For the purpose of augmentation, we conducted several distillations, including nested distillation (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We trained the classifiers with three sets of vectors: the original vectors, original vectors augmented with vectors distilled by PCA and original vectors augmented with distilled PCA vectors and double distilled by PCA vectors. The experimental results are presented in the paper, and they confirm the advantage of the PCA-distilled vectors increasing the classification statistics of ML methods if the distilled vectors augment the original training vectors.

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Section: Neural Network (Nn)mentioning

confidence: 99%

Section: Neural Network (Nn)mentioning

confidence: 99%

Section: Neural Network (Nn)mentioning

confidence: 99%

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Training Data Augmentation with Data Distilled by Principal Component Analysis

Sirakov,

Shahnewaz,

Nakhmani

2024

Electronics

Self Cite

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“…Note that the image is a 2D signal and could be presented in the form of 1D signal or as a sequence. Despite this, we could roughly divide the NNs to those which are designed to work with images [20] and those designed to work with signals and sequences of numbers [18,19]. A well-established representative of the first kind is the convolutional NN (CNN) [20], while examples of the second kind include the long-short term memory NNs (LSTM) [18,19].…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…Despite this, we could roughly divide the NNs to those which are designed to work with images [20] and those designed to work with signals and sequences of numbers [18,19]. A well-established representative of the first kind is the convolutional NN (CNN) [20], while examples of the second kind include the long-short term memory NNs (LSTM) [18,19]. The main hyperparameters of a NN consist of: architecture, input, hidden, output layers, activation functions, loss function, optimization method, and training process.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Training Data Augmentation with Data Distilled by the Principal Component Analysis

Sirakov,

Shahnewaz,

---

2023

Preprint

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This work develops a new method for vector data augmentation. The proposed method applies the Principal Component Analysis (PCA) and determines the Eigenvectors of a set of training vectors for a Machine Learning (ML) method and uses them to generate the distilled vectors. The training and the PCA distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors the extended set trains a ML classifier to achieve a required accuracy. Hence, the novelty of this study is, the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: Neural Network, Logistic Regression, Support Vector Machine with linear and polynomial kernels. For the purpose of augmentation, we conduct several distillations including nested (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We train the classifiers with three sets of vectors: original vectors; original augmented with vectors distilled by PCA; original augmented with distilled and double distilled, by PCA, vectors. The experimental results are presented in the paper, and they confirm the advantage that the PCA distilled vectors increase the classification statistics of ML methods, if the distilled vectors augment the original training vectors.

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Deep learning-based cutting force prediction for machining process using monitoring data

Lee

Kim

et al. 2023

Pattern Anal Applic

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Deep learning for location prediction on noisy trajectories

Cited by 4 publications

References 34 publications

Training Data Augmentation with Data Distilled by Principal Component Analysis

Training Data Augmentation with Data Distilled by Principal Component Analysis

Training Data Augmentation with Data Distilled by the Principal Component Analysis

Deep learning-based cutting force prediction for machining process using monitoring data

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