ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regressio

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Electroencephalographic (EEG) signals are acquired non-invasively from electrodes placed on the scalp. Experts in the field can use EEG signals to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects using classification models. However, the training of deep learning or machine learning models requires a large number of trials. Datasets related to Alzheimer’s disease are typically small in size due to the lack of AD patient samples. The lack of data samples required for the training process limits the use of deep learning techniques for further development in clinical settings. We propose to increase the number of trials in the training set by means of a decomposition–recombination system consisting of three steps. Firstly, the original signals from the training set are decomposed into multiple intrinsic mode functions via multivariate empirical mode decomposition. Next, these intrinsic mode functions are randomly recombined across trials. Finally, the recombined intrinsic mode functions are added together as artificial trials, which are used for training the models. We evaluated the decomposition–recombination system on a small dataset using each subject’s functional connectivity matrices as inputs. Three different neural networks, including ResNet, BrainNet CNN, and EEGNet, were used. Overall, the system helped improve ResNet training in both the mild AD dataset, with an increase of 5.24%, and in the mild cognitive impairment dataset, with an increase of 4.50%. The evaluation of the proposed data augmentation system shows that the performance of neural networks can be improved by enhancing the training set with data augmentation. This work shows the need for data augmentation on the training of neural networks in the case of small-size AD datasets.Fil: Jia, Hao. Universitat de Vic; España. Nankai University; ChinaFil: Huang, Zihao. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Duan, Feng. Nankai University; ChinaFil: Zhang, Yu. Lehigh University; Estados UnidosFil: Sun, Zhe. Juntendo University; ChinaFil: Solé Casals, Jordi. Universitat de Vic; Españ

Independent component analysis for naive bayes classification

Ph.DDOCTOR OF PHILOSOPH

Redução de ruído em sensores de monitoramento usando separação cega de fontes

Blind Source Separation (BSS) is known to be an efficient and powerful process to separate and estimate individual mutually independent signals acquired by various types of monitoring sensors. Theses monitoring sensors capture signals that are composed of various types of sources, the desired sources, unwanted sources and noisy sources. Thus, the desired signal is compromised so that it can be analyzed, this can lead to inefficient decision making. Ideally, the analyzed signals should be composed of the higher level of desired sources, and lower level of unwanted sources and noisy sources. This paper proposes an algorithm to identify and reduce noise in monitoring sensor signals using Blind Source Separation. This algorithm can be applied in any area of monitoring. It can identify noise without any kind of previous information of the signal analyzed. Initially, the algorithm makes the separation of the signals that were acquired by the sensors. These signals may have suffered influence from several noise sources. Different from the standard BSS, which requires at least two sources, this algorithm removes the noise from each signal separately applying the Maximum Signal-to-Noise Ratio and Temporal Predictability algorithms. The proposed algorithm also produces two outputs for each signal, the estimated original signal and the estimated noise. The results satisfy all the proposed objectives of this work. All the metrics used as parameters to evaluate the results obtained by the proposed algorithm were satisfactory. Specifically, for the thermal profile data, the most interesting results were the thermal gradients and their respective standard deviations, which showed a significant improvementTese (Doutorado)Separação Cega de Fonte (BSS) é conhecida por ser um processo eficiente e poderoso em separar e estimar sinais mutuamente independentes adquiridos por vários tipos de sensores de monitoramento. Esses sensores de monitoramento captam sinais que são compostos por vários tipos de fontes, as fontes desejadas, as fontes indesejadas e as fontes ruidosas. Assim, o sinal desejado está comprometido para que possa ser analisado, isso pode levar a tomadas de decisões não eficientes. O ideal seria que os sinais analisados fossem compostos do maior nível de fontes desejadas, e menor nível de fontes indesejadas e fontes ruidosas. Este trabalho propõe um algoritmo para identificar e reduzir os níveis de ruído em sinais monitorados por sensores usando Separação Cega de Fonte. Este algoritmo pode ser aplicado em várias áreas de monitoramento. Ele é capaz de identificar o ruído sem qualquer tipo de informação prévia do sinal analisado. Inicialmente, o algoritmo realiza uma separação dos sinais que foram monitorados por sensores. Estes sinais podem ter sofrido influência (interferência) de seus sensores vizinhos. Diferentemente da BSS padrão, que requer pelo menos duas fontes, este algoritmo reduz os níveis de ruído de cada fonte separadamente aplicando os algoritmos de Taxa Máxima Sinal Ruído e Previsibilidade Temporal. O algoritmo proposto também produz duas saídas para cada sinal (fonte), o sinal original estimado e o ruído estimado. Os resultados satisfazem todos os objetivos proposto neste trabalho. Todas as métricas utilizadas como parâmetros (SNR – Relação Sinal Ruído, SDR – Relação Sinal Distorção e SIR – Relação Sinal Interferência) de avaliação dos resultados obtidos pelo algoritmo proposto foram satisfatórias. Em específico, para os dados de perfilagem térmica, os resultados mais interessantes foram os gradientes térmicos e seus respectivos desvios padrões, que apresentaram maior ganho de precisão