منابع مشابه
ICA mixture models for image processing
We apply a probabilistic method for learning efficient image codes to the problem of unsupervised classification, segmentation and de-noising of images. The method is based on the Independent Component Analysis (ICA) mixture model proposed for unsupervised classification and automatic context switching in blind source separation [I]. In this paper, we demonstrate that this algorithm is effectiv...
متن کاملUnsupervised Classiication with Non-gaussian Mixture Models Using Ica
We present an unsupervised classiication algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are generated by a linear mixture of independent sources. The algorithm nds the independent sources, the mixing matrix for each class and also computes the clas...
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We present in this communication a procedure to extent ICA mixture models (ICAMM) to the case of having sequential dependence in the feature observation record. We call it sequential ICAMM (SICAMM). We present the algorithm, essentially a sequential Bayes processor, which can be used to sequentially classify the input feature vector among a given set of possible classes. Estimates of the class-...
متن کاملIca Mixture Models for Unsupervised Classification and Automatic Context Switching
We present an unsupervised classification algorithm based on an ICA mixture model. A mixture model is a model in which the observed data can be categorized into several mutually exclusive data classes. In an ICA mixture model, it is assumed that the data in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources and the mixing matrix for ...
متن کاملProbabilistic PCA and ICA Subspace Mixture Models for Image Segmentation
High-dimensional data, such as images represented as points in the space spanned by their pixel values, can often be described in a significantly smaller number of dimensions than the original. One of the ways of finding lowdimensional representations is to train a mixture model of principal component analysers (PCA) on the data. However, some types of data do not fulfill the assumptions of PCA...
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2019
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2018.10.003