In this paper, a new feature extraction algorithm considering both two-directional two-dimensional principal component analysis ((2D)2PCA) and independent component analysis(ICA), called (2D)2PCA-ICA, is proposed for face representation. This algorithm analyzes the principal components of image vectors on 2D matrices by simultaneously considering the row and column directions as opposed to the ...

In the present contribution we tackle the problem of nonlinear independent component analysis by non-Euclidean Hebbian-like learning. Independent component analysis (ICA) and blind source separation originally were introduced as tools for the linear unmixing of the signals to detect the underlying sources. Hebbian methods became very popular and succesfully in this context. Many nonlinear ICA e...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

In this paper, the problem of target detection in colocated “multi-input multi-output” (MIMO) radars is considered. A pulse-train signaling is assumed to be used in this system. As the doppler effect should be considered for the pulse-train signaling, we are confronted by a compound hypothesis testing problem, so in this paper a Generalized Likelihood Ratio (GLR) detector is derived. The high c...

Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information. Its sources have more compact representations than the sour...

As the Internet spreads widely, it has become easier for companies to obtain and utilize valuable information on their customers. Nevertheless, many of them have difficulty in using the information effectively because of the huge amount of data from their customers that must to be analyzed. In addition, the data usually contains much noise due to anonymity of the Internet. Consequently, extract...

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, h...

As a new approach of blind source separation (BSS), independent component analysis (ICA) has attracted extensive attention of researchers in the field of information processing. In this paper, the basic theory and algorithm of ICA are briefly introduced, and then ICA is used for the preprocessing of engine acoustic signals to identify the engine noise sources. The ICA decomposes the signals int...

View NONINVASIVE IMAGING OF INDEPENDENT CORTICAL FLOW PATTERNS. While independent component analysis (ICA) is useful for modeling brain and electroencephalographic (EEG) data, current ICA methods for EEG model signal sources as acting in perfect synchrony within spatially fixed domains. In contrast, invasive animal recordings have observed waves of neuronal activity propagating quickly across m...

Cocktail-party Problems (increasing generality): • Independent component analysis (ICA) [1, 2]: onedimensional sound sources. • Independent subspace analysis (ISA) [3]: independent groups of people. • Blind source deconvolution (BSD) [4]: one-dimensional sound sources and echoic room. • Blind subspace deconvolution (BSSD) [5]: independent source groups and echoes. Separation Theorem: • ISA ([3]...