Permutation ambiguity of the classical ICA may cause problems in feature extraction for pattern classification. To solve that, we include a selective prior for de-mixing coefficients into the classical ICA. Since the prior is constructed upon the classification information from the training data, we refer to the proposed ICA model with a selective prior as a supervised ICA. We formulate the lea...

Independent component analysis (ICA) is a method to estimate components which are as statistically independent as possible. However, in many practical applications, the estimated components are not independent. Recent variants of ICA have made use of such residual dependencies to estimate an ordering (topography) of the components. Like in ICA, the components in those variants are assumed to be...

Independent component analysis (ICA) The ICA algorithm has been applied successfully to modeling V1 simple cell receptive fields [1, 2]. It is closely related to sparse coding methods, and can be cast in terms of a simple generative model [3]: We suppose that our data x ∈ R is an unknown linear mixture of independent, non-Gaussian sources, i.e. x = As where A ∈ Rn×n is unknown. During learning,...

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. ...

Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using Independent Component Analysis (ICA). An ICA algorithm is tested on 3−channel ECG recordings taken from human subjects, mostly...

Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...

Methods for obtaining representations of face images based on independent component analysis (ICA) are presented. A global ICA representation is compared to a global representation based on principal component analysis (PCA) for recognizing faces across changes in lighting and changes in pose. For each set of face images, a set of statistically independent source images was found through an uns...

Geometric algorithms for linear independent component analysis (ICA) have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA has been proposed first by Puntonet and Prieto [6] in order to separate linear mixtures. One major drawback of geometric algorithms is, however, an exponentially rising number of sam...

Despite an increased interest in complex independent component analysis (ICA) during the last two decades, a closed form expression for the Cramér-Rao bound (CRB) for the demixing matrix is not known yet. In this paper, we fill this gap by deriving a closed-form expression for the CRB of the demixing matrix for instantaneous noncircular complex ICA. It contains the CRB for circular complex ICA ...