In this paper we present a new scheme for Palmprint verification. The proposed method can be viewed as a combination of Gaussian Mixture Model (GMM) followed by Independent Component Analysis (ICA I and ICA II) applied directly on the pixels. This approach follows the path opened by previous works making use of GMM followed by Principal Component Analysis (PCA) and Linear Discriminate Analysis ...

SUMMARY Independent component analysis (ICA) has recently been applied to epileptic seizure in the EEG. In this paper, the authors show how the fundamental axioms required for ICA to be valid are broken. Four common cases of childhood seizure are presented and assessed for stationarity and an eigenvalue analysis is applied. In all cases, for the stationary sections of data the eigenvalue analys...

The features of human lip motion from video clips are extracted by three unsupervised learning algorithms, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Since the human perception of facial motion goes through two different pathways, i.e., the lateral fusifom gyrus for the invariant aspects and the superior temporal ...

<p><span>Alzheimer merupakan salah satu jenis penyakit demensia yang ditandai dengan penurunan fungsi otak secara perlahan mulai dari ingatan sampai pada fisik. Diagnosis Alzheimer dapat dilakukan melalui analisis sinyal hasil rekaman EEG (Electroencephalogram). Namum, masalah utama dihadapi dalam memahami adalah terukur campuran antara dan <em>artifact.</em> <em>A...

Independent component analysis (ICA) of an image sequence extracts a set of statistically independent images, and deenes a corresponding set of unconstrained dual time courses. However, the extra degrees of freedom implicit in these time courses can lead to physically improbable solutions. Accordingly, we introduce two methods for regularising ICA: smoothed independent component analysis (smICA...

Model free exploratory methods available for functional MRI analysis of one subject such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are investigated for generalisations when analysing more than one subject. Introducing the subject dimension makes a three-way data: brain, time, subject, and multiway methods are proposed either to optimise the variance (PCA) or...

Face recognition is emerging as an active research area with numerous commercial and law enforcement applications. This paper presents comparative analysis of two most popular subspace projection techniques for face recognition. It compares Principal Component Analysis (PCA) and Independent Component Analysis (ICA), as implemented by the InfoMax algorithm. ORL face database is used for training...

This paper extends the framework of independent component analysis (ICA) to supervised learning. The key idea is to find a conditionally independent representation of input variables for given output. The representation is useful for the naive Bayes learning which has been reported to perform as well as more sophisticated methods. The learning algorithm is derived in a similar criterion to ICA....

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

Independent component analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy independent component analysis, referred to as...