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Using EEG signal as a new type of biometric in user authentication systems has been emerging as an interesting research topic. However, one of the major challenges is that a huge amount of EEG data that needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. In this paper, we investigate the feasibility of using lossy compression to EEG data in EEG-based...
Stochastic Gradient Descent (SGD) based method offers a viable solution to training large-scale dataset. However, the traditional SGD-based methods cannot get benefit from the distribution or geometry information carried in data. The reason is that these methods make use of the uniform distribution over the entire training set so as to sample the next data point for updating the model. We address...
Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the...
In this paper, we propose a feature-based approach to address the challenging task of recognising overlapping sound events from single channel audio. Our approach is based on our previous work on Local Spectrogram Features (LSFs), where we combined a local spectral representation of the spectrogram with the Generalised Hough Transform (GHT) voting system for recognition. Here we propose to take the...
For the Multi-label Bird Species Classification task in NIPS 2013, we design an ensemble classifier using simple audio features extracted from the given audio clips. The main difference of our approach is that we do not segment the audio or use local features and still achieve comparable results to the top teams of the challenge utilizing complex engineered features.
Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a data description for a dataset while support vector classification (SVC) aims at separating data of two classes without providing a data description. This paper proposes a unified approach to both SVDD and SVC that aims at separating data of two classes and at the same time provides a data description...
The paper presents a novel approach for solving face recognition problem. We combine Gabor filters and Principal Component Analysis (PCA) to extract feature vectors; then we apply Support Vector Machine (SVM), the most powerful discriminative method, and AdaBoost, a meta-algorithm, for classification. Experiments for the proposed method have been conducted on two public face database AT&T and...
Ambulatory devices can be used to detect heart diseases and save lives in critical time. These devices are based on sound classification that usually adopts a suitable data mining algorithm. This paper investigates the performance of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers in classifying sound samples. SVM classifier makes use of a linearly separable hyperplane to...
Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the...
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