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Spectral clustering consists in creating, from the spectral elements of a Gaussian affinity matrix, a low-dimensional space in which data are grouped into clusters. The performance of the spectral clustering is mainly depended upon the construction of the similarity function. The most commonly used similarity function is Gaussian similarity function. However, questions about the separability of clusters...
This paper presents an algorithm for interactive adaptation to new user using dynamically calculated principal components. In this algorithm, new user is adapted based on the matching score of face recognition method that use dynamically calculated eigenvectors and eigenvalues from known training face dataset. User adaptation method measures the trueness of known and unknown person using the recognition...
Verification decisions are often based on second order statistics estimated from a set of samples. Ongoing growth of computational resources allows for considering more and more features, increasing the dimensionality of the samples. If the dimensionality is of the same order as the number of samples used in the estimation or even higher, then the accuracy of the estimate decreases significantly....
An algorithm for optimizing the principal component analysis in gesture recognition is proposed, which makes use of covariance between factors to reduce data dimensions. The objectivity and automatization of above manual observation is realized by algorithm. We present an approach for the detection and identification of human gestures and describe a working, near gesture recognition system and then...
It is known to all that obtaining an effectual feature representation is of paramount importance to face recognition. In this paper, the latest feature extraction method based on KCCA is introduced. However, in the training stage of the standard KCCA-based extractor, it requires to store and manipulate the kernel matrix, the size of which is square of the number of samples. When the sample numbers...
The effect of noise and occlusion on parametric eigenspace is studied in depth in this paper in order to identify the response of the system for distorted inputs from different sources and environmental factors. The eigenspace method of Murase become very popular due to its ability of automatic visual learning and power to capture the parametric details of the object with low memory. The work done...
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