Robust dimensionality reduction for data visualization with deep neural networks

Becker, Martin; Lippel, Jens; Stuhlsatz, André; Zielke, Thomas

doi:10.1016/j.gmod.2020.101060

Cited by 28 publications

(16 citation statements)

References 22 publications

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“…When developing machine learning models (such as ANNs), at a certain point, more features or dimensions can decrease model's accuracy (a phenomenon called ‘the curse of dimensionality’) ( Han et al, 2020 ). In this case, a dimensionality reduction strategy (such as the utilization of PCA) may be employed in order to accelerate the training time of the model, reduce its complexity and avoid overfitting ( Becker et al, 2020 ). In general, PCA projects the original data onto a new set of few variables called principal components (PCs).…”

Section: Methodsmentioning

confidence: 99%

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy

Kapourani

Valkanioti

Kontogiannopoulos

et al. 2020

International Journal of Pharmaceutics: X

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The objective of the present study was to evaluate the use of artificial neural networks (ANNs) in the development of a new chemometric model that will be able to simultaneously distinguish and quantify the percentage of the crystalline and the neat amorphous drug located within the drug-rich amorphous zones formed in an amorphous solid dispersion (ASD) system. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy was used, while Rivaroxaban (RIV, drug) and Soluplus® (SOL, matrix-carrier) were selected for the preparation of a suitable ASD model system. Adequate calibration and test sets were prepared by spiking different percentages of the crystalline and the amorphous drug in the ASDs (prepared by the melting - quench cooling approach), while a 2 4 full factorial experimental design was employed for the screening of ANN's structure and training parameters as well as spectra region selection and data preprocessing. Results showed increased prediction performance, measured based on the root mean squared error of prediction (RMSEp) for the test sample, for both the crystalline (RMSEp (crystal) = 0.86) and the amorphous (RMSEp (amorphous) = 2.14) drug. Comparison with traditional regression techniques, such as partial least square and principle component regressions, revealed the superiority of ANNs, indicating that in cases of high structural similarity between the investigated compounds (i.e., the crystalline and the amorphous forms of the same compound) the implementation of more powerful/sophisticated regression techniques, such as ANNs, is mandatory.

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Section: Methodsmentioning

confidence: 99%

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy

Kapourani

Valkanioti

Kontogiannopoulos

et al. 2020

International Journal of Pharmaceutics: X

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“…The evident example of this approach is the use of kernel functions in SVM classifiers, aiming to improve the separability property to allow efficient localization of the hyperplane [14]. Another example is the latent representation yielded on the hidden layers of ANNs [15,16]. The goal is to increase the compactness of the class samples by abstracting the input data from its specifics and details.…”

Section: Introductionmentioning

confidence: 99%

Feature Transformation Framework for Enhancing Compactness and Separability of Data Points in Feature Space for Small Datasets

2022

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Compactness and separability of data points are two important properties that contribute to the accuracy of machine learning tasks such as classification and clustering. We propose a framework that enhances the goodness criteria of the two properties by transforming the data points to a subspace in the same feature space, where data points of the same class are most similar to each other. Most related research about feature engineering in the input data points space relies on manually specified transformation functions. In contrast, our work utilizes a fully automated pipeline, in which the transformation function is learnt via an autoencoder for extraction of latent representation and multi-layer perceptron (MLP) regressors for the feature mapping. We tested our framework on both standard small datasets and benchmark-simulated small datasets by taking small fractions of their samples for training. Our framework consistently produced the best results in all semi-supervised clustering experiments based on K-means and different seeding techniques, with regards to clustering metrics and execution time. In addition, it enhances the performance of linear support vector machine (LSVM) and artificial neural network (ANN) classifier, when embedded as a preprocessing step before applying the classifiers.

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“…The ReNDA algorithm [15] is a very recent neural-based approach that uses two networks, improving on earlier work from the same authors. One network is used to implement a nonlinear generalization of Fisher's Linear Discriminant Analysis, using a method called GerDA; the other network is an Autoencoder used as a regularizer.…”

Section: 2mentioning

confidence: 99%

“…Typically, autoencoders produce results comparable to PCA. The ReNDA algorithm [15] uses two networks, improving on…”

Section: 2mentioning

confidence: 99%

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Learning Multidimensional Projections with Neural Networks

Espadoto¹

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Robust dimensionality reduction for data visualization with deep neural networks

Cited by 28 publications

References 22 publications

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy

Feature Transformation Framework for Enhancing Compactness and Separability of Data Points in Feature Space for Small Datasets

Learning Multidimensional Projections with Neural Networks

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