FACE RECOGNITION — A GENERALIZED MARGINAL FISHER ANALYSIS APPROACH

The recently proposed Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problem and the optimal basis vectors obtained by the MFA are nonorthogonal. In this paper, we present a new method called Direct Orthogonal Marginal Fisher Analysis (DOMFA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space without pre-processing using PCA and does not suffer the small sample size problem. Experimental results on ORL database indicate that the proposed DOMFA method achieves better recognition rate than the MFA method and some other orthogonal feature extraction algorithms.

In recent years, a variety of manifold-based learning dimensionality reduction techniques have been proposed, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, marginal fisher analysis (MFA) achieved high performance for face recognition. However, the optimal basis vectors obtained by MFA are non-orthogonal and MFA usually deteriorates when labeled information is insufficient. In order to resolve these problems, we present a new method called orthogonal semi-supervised marginal fisher analysis (OSMFA), which not only extracts all the orthogonal discriminant vectors but also preserves the global structure of labeled and unlabeled samples to learn a better subspace for classification. Experimental results on ORL database demonstrate the effectiveness of the proposed algorithm.

The dynamic performance of voltage source converter-based multi-terminal high-voltage direct current (VSC-MTDC) grid for large-scale renewable energy integration is becoming a concern. This study proposes a system-level dynamic response model of wind farm (WF)-MTDC-main AC systems, and then analyses the dynamic performance based on the singular value decomposition (SVD) technique. In the modelling, an improved virtual synchronous machine control is developed, and the interaction between AC frequency and DC voltage can be readily described. Using the SVD technique, parameters of the controllers are tuned, thereby making the AC frequency and DC voltage deviations within the limitations. Some oscillation modes of the system are observed and virtual power system stabiliser is proposed to suppress the oscillations. Additionally, the oscillation can be mitigated by emulating capacitance-based inertia response. The efficiency of the proposed model and analysis is verified through the frequency-domain and time-domain results.

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

We present a generalized singular value decomposition (GSVD) based multi-user (MU) multi-stream multiple-input multiple-output (MIMO) precoding scheme with limited feedback. Our precoding scheme maximizes the signal to leakage and noise ratio (SLNR), so that it does not require any iterations to obtain precoding matrices. However, in MU-MIMO system, the precoding matrix should be computed at transmit side, because the precoding matrix of a user affects those of other users. Accordingly, full channel state information (CSI) for all users is required at the transmitter, which prevents MU-MIMO precoding implementable. To overcome this problem, an efficient limited feedback algorithm optimized to the GSVD based MU-MIMO precoding is proposed. Here, by extracting the parameters to be utilized in GSVD based precoding from the full CSI, the amount of feedback information is reduced. Furthermore, by analyzing their statistical properties, the parameters are efficiently quantized according to their probability densities. Finally, we develop a modified GSVD based precoding considering the channel quantization errors due to the limited feedback.

Relevance and irrelevance graph based marginal Fisher analysis for image search reranking

This paper proposes an algorithm for three-phase harmonic state estimation for power distribution networks, by using the Singular Value Decomposition (SVD) technique. As non-linear loads are distributed throughout the network, state estimation-based tools become relevant to assess the impacts of the harmonics on the grid. Moreover, the SVD technique is able to estimate the state with a reduced number of meters and, if the estimation is not possible, it indicates where monitors should be placed in order to provide full observability. The results were quite satisfactory when applied to the IEEE 13 node test system.

Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.

A novel approach for multimicrophone speech dereverberation is presented. The method is based on the construction of the null subspace of the data matrix in the presence of colored noise, using the generalized singular-value decomposition (GSVD) technique, or the generalized eigenvalue decomposition (GEVD) of the respective correlation matrices. The special Silvester structure of the filtering matrix, related to this subspace, is exploited for deriving a total least squares (TLS) estimate for the acoustical transfer functions (ATFs). Other less robust but computationally more efficient methods are derived based on the same structure and on the QR decomposition (QRD). A preliminary study of the incorporation of the subspace method into a subband framework proves to be efficient, although some problems remain open. Speech reconstruction is achieved by virtue of the matched filter beamformer (MFBF). An experimental study supports the potential of the proposed methods.

This paper develops a supervised discriminant technique based on graph embedding, called maximum inter-class and marginal discriminant embedding (MIMDE) for feature and classification in high-dimensional data space. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible through local property (intra-class compactness graph and inter-class separability graph) and marginal property (margin separability graph). This characteristic makes MIMDE more intuitive and more powerful than marginal Fisher analysis which not considered inter-class separability graph. Experimental results on ORL and AR face databases show the effectiveness of the proposed method.

Modeling and forecasting human mortality are significant research topics in several disciplines because mortality rates are fundamental in planning and policy decisions. Among various techniques, Lee Carter (LC) model is one of the most popular stochastic method in human mortality modeling. The original LC model was fuzzified to eliminate the assumptions related with homoscedasticity. The existing fuzzy model makes use of ordinary least squares (OLS) technique, which prevents the model to capture the existing fluctuations in data. In this study, a revised version of fuzzy LC model utilizing singular value decomposition (SVD) technique is proposed to overcome this issue. After modeling the mortality rates, their future values are forecasted by a modified first order fuzzy time series technique. For illustration purposes, proposed method is applied to mortality data of Finland. Numerical outputs show that proposed method is statistically better in modeling mortality compared to the existing fuzzy method. In addition, the modified fuzzy time series technique generates better forecasts than the original version.

Face recognition has been widely used to identify faces according to the patterns of mouth, lip, eyes and so on. Existing face recognition techniques provide a reasonable accuracy. However, their effectiveness significantly degrades in presence of uncontrolled conditions such as variations of background light and image sizes. To deal with this problem, we propose a novel approach based on Classified Vector Quantization (CVQ) technique for face recognition. The new approach divides images into some blocks and each block is classified into several patterns. The Vector Quantization technique (VQ) is applied on the vectors of each pattern. A major benefit of this technique is to consider the geometric structure of face and skin correlations which provide recognition based on changes on human's age. In order to evaluate our approach, experiments on some standard image databases have been conducted. The results demonstrate the high recognition rate and accuracy of our technique.

Marginal Fisher Analysis(MFA) is a typical supervised subspace embedding method which has been used in dimensionality reduction. The projection matrixes are obtained by maximizing the intraclass compactness and simultaneously minimizing the intraclass separability. But in practical applications, no sufficient labeled training samples with prior knowledge was provided, so unlabeled image data are eager for incorporating in subspace learning algorithm to improve the identification accuracy. In this paper, we propose a semi supervised learning algorithm, which is called semi-supervised Marginal Fisher Analysis(SMFA). Not only the labeled data points are used to maximize the separability between different classes, but also the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Therefore, we design a discriminant function which is as smooth as possible on the data manifold. Experimental results demonstrate that our SMFA algorithm outperforms the start-of-art methods.

Abstract. The electron density distribution of the low- and mid-latitude ionosphere has been investigated by the computerized tomography technique using a Generalized Singular Value Decomposition (GSVD) based algorithm. Model ionospheric total electron content (TEC) data obtained from the International Reference Ionosphere 2001 and slant relative TEC data measured at a chain of three stations receiving transit satellite transmissions in Alaska, USA are used in this analysis. The issue of optimum efficiency of the GSVD algorithm in the reconstruction of ionospheric structures is being addressed through simulation of the equatorial ionization anomaly (EIA), in addition to its application to investigate complicated ionospheric density irregularities. Results show that the Generalized Cross Validation approach to find the regularization parameter and the corresponding solution gives a very good reconstructed image of the low-latitude ionosphere and the EIA within it. Provided that some minimum norm is fulfilled, the GSVD solution is found to be least affected by considerations, such as pixel size and number of ray paths. The method has also been used to investigate the behaviour of the mid-latitude ionosphere under magnetically quiet and disturbed conditions.

In this paper we examine the use of generalized singular value decomposition (GSVD) for coordinated beamforming in MIMO systems. GSVD facilitates joint decomposition of a class of matrices arising inherently in source-to-2 destination MIMO broadcast scenarios. GSVD allows two channels of suitable dimensionality to be jointly diagonalized, i.e. to be reduced to non-interfering virtual broadcast channels, through the use of jointly determined transmit precoding and receiver reconstruction matrices. Potential applications for GSVD-based beamforming can be found in MIMO broadcasting, as well as in MIMO relaying under all amplify-and-forward, decode-and-forward, and code-and-forward relay processing schemes. Several of them are highlighted here. We also present simulation-based performance analysis results to justify the use of GSVD for coordinated beamforming.