Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with multidimensional data (tensors) directly via tensor-to-tensor projection or tensor-to-vector projection (TVP). However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthog...

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low rank structure of the data. In the high-dimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA ...

This paper describes a comparative analysis of recognition accuracy using feature extraction algorithm. A feature extraction algorithm is introduced for face recognition, Principle Component Analysis (PCA),Linear Discriminant Analysis(LDA) , Independent Component Analysis(ICA) and Nonnegative matrix factorization (NMF) based on curvelet transform. Mostly recognition system is capable to perform...

Human face is contexture multidimensional point of vision model and by creating computational model for human face recognition is too hard. The paper present two methodologies for the face recognition, the first one is feature extraction and second is the feed forward back propagation neural network. The feature extraction is with Principal Component Analysis and classification with the help of...

This paper reports a study to unveil the quality relevant perceptual space of video degradations in the domain of video telephony. The perceptual space was explored using a Semantic Differential (SD) test paradigm with a subsequent Principal Component Analysis (PCA). This paper provides a view on the test itself as well as on the analysis of the results. Keywords—quality of experience; video qu...

Recognizing human age group automatically through facial image analysis has many applications, such as human computer interaction and multimedia communication. The aging process involves many factors such as the person’s gene, health, living style, living location and weather conditions. This paper presents an automatic human age group Recognition system based on human facial images. Features a...

Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Ana...

In the presence of outliers, the existing self-organizing rules for Principal Component Analysis (PCA) perform poorly. Using statistical physics techniques including the Gibbs distribution, binary decision fields and effective energies, we propose self-organizing PCA rules which are capable of resisting outliers while fulfilling various PCA-related tasks such as obtaining the first principal co...

In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients charact...