In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.

This study proposes a variable selection linear regression (VSLR) adaptation framework to improve the accuracy of automatic speech recognition (ASR) with only limited and unlabeled adaptation data. The proposed framework can be divided into three phases. The first phase prepares multiple variable subsets by applying a ranking filter to the original regression variable set. The second phase determines the best variable subset based on a pre-determined performance evaluation criterion and computes a linear regression (LR) mapping function based on the determined subset. The third phase performs adaptation in either model or feature spaces. The three phases can select the optimal components and remove redundancies in the LR mapping function effectively and thus enable VSLR to provide satisfactory adaptation performance even with a very limited number of adaptation statistics. We formulate model space VSLR and feature space VSLR by integrating the VS techniques into the conventional LR adaptation systems. Experimental results on the Aurora-4 task show that model space VSLR and feature space VSLR, respectively, outperform standard maximum likelihood linear regression (MLLR) and feature space MLLR (fMLLR) and their extensions, with notable word error rate (WER) reductions in a per-utterance unsupervised adaptation manner.

This study proposes a feature-level fusion method that uses finger veins (FVs) and finger dorsal texture (FDT) for personal authentication based on orientation selection (OS). The orientation codes obtained by the filters correspond to different parts of an image (foreground or background) and thus different orientations offer different levels of discrimination performance. We have conducted an orientation component analysis on both FVs and FDT. Based on the analysis, an OS scheme is devised which combines the discriminative orientation features of both modalities. Our experiments demonstrate the effectiveness of the proposed method.

As a next-generation video compression standard, High Efficiency Video Coding (HEVC) achieves enhanced coding performance relative to prior standards such as H.264/AVC. In the new standard, the improved intra prediction plays an important role in bit rate saving. Meanwhile, it also involves significantly increased complexity, due to the adoption of a highly flexible coding unit structure and a large number of angular prediction modes. In this paper, we present a low-complexity intra prediction algorithm for HEVC. We first propose a fast preprocessing stage based on a simplified cost model. Based on its results, a fast prediction unit selection scheme reduces the number of prediction unit (PU) levels that requires fine processing from 5 to 2. To supply PU size decision with appropriate thresholds, a fast training method is also designed. Still based on the preprocessing results, an efficient mode selection scheme reduces the maximum number of angular modes to evaluate from 35 to 8. This achieves further algorithm acceleration by eliminating the necessity to perform fine Hadamard cost calculation. We also propose a 32×32 PU compensation scheme to alleviate the mismatch of cost functions for large transform units, which effectively improves coding performance for high-resolution sequences. In comparison with HM 7.0, the proposed algorithm achieves over 50% complexity reduction in terms of encoding time, with the corresponding bit rate increase lower than 2.0%. Moreover, the achieved complexity reduction is relatively stable and independent to sequence characteristics.

Observed samples in wideband radar are always represented as nonlinear points in high dimensional space. In this paper, we consider the feature selection problem in the scenario of wideband radar target clustering. Inspired by manifold learning, we propose a novel feature selection algorithm, called Local Reconstruction Error Alignment (LREA), to select the features that can best preserve the underlying manifold structure. We first select the features that minimize the reconstruction error in every neighborhood. Then, we apply the alignment technique to extend the local optimal feature sequence to a global unique feature sequence. Experiments demonstrate the effectiveness of our proposed method.

Even with the recent development of new types of social networking services such as microblogs, Bulletin Board Systems (BBS) remains popular for local communities and vertical discussions. These BBS sites have high volume of traffic everyday with user discussions on a variety of topics. Therefore it is difficult for BBS visitors to find the posts that they are interested in from the large amount of discussion threads. We attempt to explore several main characteristics of BBS, including organizational flexibility of BBS texts, high data volume and aging characteristic of BBS topics. Based on these characteristics, we propose a novel method of Online Topic Detection (OTD) on BBS, which mainly includes a representative post selection procedure based on Markov chain model and an efficient topic clustering algorithm with candidate topic set generation based on Aging Theory. Experimental results show that our method improves the performance of OTD in BBS environment in both detection accuracy and time efficiency. In addition, analysis on the aging characteristic of discussion topics shows that the generation and aging of topics on BBS is very fast, so it is wise to introduce candidate topic set generation strategy based on Aging Theory into the topic clustering algorithm.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

Recently, fuzzy set theory has been widely employed in building portfolio selection models where uncertainty plays a role. In these models, future security returns are generally taken for fuzzy variables and mathematical models are then built to maximize the investment profit according to a given risk level or to minimize a risk level based on a fixed profit level. Based on existing works, this paper proposes a portfolio selection model based on fuzzy birandom variables. Two original contributions are provided by the study: First, the concept of technical analysis is combined with fuzzy set theory to use the security returns as fuzzy birandom variables. Second, the fuzzy birandom Value-at-Risk (VaR) is used to build our model, which is called the fuzzy birandom VaR-based portfolio selection model (FBVaR-PSM). The VaR can directly reflect the largest loss of a selected case at a given confidence level and it is more sensitive than other models and more acceptable for general investors than conventional risk measurements. To solve the FBVaR-PSM, in some special cases when the security returns are taken for trapezoidal, triangular or Gaussian fuzzy birandom variables, several crisp equivalent models of the FBVaR-PSM are derived, which can be handled by any linear programming solver. In general, the fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO) is designed to find the approximate optimal solution. To illustrate the proposed model and the behavior of the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide a discussion of some existing approaches.

Text sentiment classification aims to automatically classify subjective documents into different sentiment-oriented categories (e.g. positive/negative). Given the high dimensionality of features describing documents, how to effectively select the most useful ones, referred to as sentiment-bearing features, with a lack of sentiment class labels is crucial for improving the classification performance. This paper proposes an unsupervised sentiment-bearing feature selection method (USFS), which incorporates sentiment discriminant analysis (SDA) into sentiment strength calculation (SSC). SDA applies traditional linear discriminant analysis (LDA) in an unsupervised manner without losing local sentiment information between documents. We use SSC to calculate the overall sentiment strength for each single feature based on its affinities with some sentiment priors. Experiments, performed using benchmark movie reviews, demonstrated the superior performance of USFS.

Relay selection is a promising technique with which to achieve remarkable gains in multi-relay cooperative networks. Opportunistic relaying (OR) and selection cooperation (SC) are two major relay selection schemes for dual-hop decode-and-forward cooperative networks; they have been shown to be globally outage-optimal under an aggregate power constraint. However, due to channel fluctuations, the channel state information (CSI) used in the selection process may become outdated and differ from the CSI during the actual transmission of data. In this work, we study the effect of outdated CSI on OR and threshold-based SC (TSC) schemes under independent but not necessarily identically distributed Rayleigh fading channels. The source can possibly cooperate with the best relay for data transmission, with the destination performing maximal ratio combining of the signals from the source and the relay. In particular, we analyze the average symbol error probability (ASEP) of OR and TSC with outdated CSI by deriving approximate but tight closed-form expressions for the moment generating function of the end-to-end signal-to-noise ratio. We also investigate the asymptotic behavior of the ASEP. The results show that the diversity orders of OR and TSC reduce to one and two, respectively, due to the outdated CSI. However, TSC achieves full spatial diversity order when the relay-to-destination CSI is perfect. Finally, to verify the analytical results Monte Carlo simulations are performed, in which OR attains better ASEP than TSC in a perfect CSI scenario, while TSC is less susceptible to outdated CSI.

This paper studies channel sounding for selfish dynamic spectrum control (S-DSC) in which each link dynamically maps its spectral components onto a necessary amount of discrete frequencies having the highest channel gain of the common system band. In S-DSC, it is compulsory to conduct channel sounding for the entire system band by using a reference signal whose spectral components are sparsely allocated by S-DSC. Using nonuniform sampling theory, this paper exploits the finite impulse response characteristic of frequency selective fading channels to carry out the channel sounding. However, when the number of spectral components is relatively small compared to the number of discrete frequencies of the system band, reliability of the channel sounding deteriorates severely due to the ill-conditioned problem and degradation in channel capacity of the next frame occurs as a result. Aiming at balancing frequency selection diversity effect and reliability of channel sounding, this paper proposes an S-DSC which allocates an appropriate number of spectral components onto discrete frequencies with low predicted channel gain besides mapping the rest onto those with high predicted channel gain. A numerical analysis confirms that the proposed S-DSC gives significant enhancement in channel capacity performance.

In this paper, a practical antenna selection (AS) scheme is investigated for downlink multiuser massive multiple input multiple output (MIMO) networks where a base station (BS) is equipped with many antennas (N) and communicates with K mobile stations (MSs) simultaneously. In the proposed antenna selection technique, S antennas (S≤N) are selected for transmission based on the knowledge of channel coefficients of each MS for reducing the number of RF chains which mainly induce cost increase in terms of size, hardware, and power. In the proposed AS technique, a BS first ranks antenna elements according to the sum of their channel gains to all MSs. Then, the BS computes the downlink sum-rate with S consecutive antenna elements in the ordered set, where the subset consisting of S consecutive antennas is called a window. The BS selects the window resulting in the highest sum-rate. The selected S antenna elements are used for transmitting signals to multiple users, while the remaining (N-S) antenna elements are turned off for the time slot. Therefore, the proposed AS technique requires only (N-S+1) sum-rate computations, while the optimal AS technique involves $inom{N}{S}$ computations. We analyze downlink sum-rate with the proposed AS technique and compare it with that of a reference system with the same number of antenna elements without AS. Our results show that the proposed AS technique significantly outperforms the reference scheme.

In this study, we investigate the use of scheduling algorithms to support coordinated multipoint (CoMP) operation for Long Term Evolution (LTE)-Advanced systems studied in the 3rd Generation Partnership Project (3GPP). CoMP, which improves cooperative transmission among network nodes (transmission points: TPs) and reduces or eliminates interTP interference, enabling performance improvements in cell edge throughputs. Although scheduling algorithms in LTE systems have been extensively investigated from the single cell operation perspective, those extension to CoMP where each user equipment (UE) has multiple channel state information (CSI) feedbacks require further consideration on proportional fairness (PF) metric calculation while maintaining PF criteria. To this end, we propose to apply a scaling factor in accordance with the number of CSI feedbacks demanded for the UE. To evaluate the benefits of this scaling factor, multicell system-level simulations that take account of channel estimation errors are performed, and the results confirmed that our improved algorithm enables fairness to be maintained.

In this letter, a fast transmit antenna selection algorithm is proposed for the spatial-temporal combining-based spatial multiplexing ultra-wideband systems on a log-normal multipath fading channel. The presented suboptimum algorithm selects the transmit antennas associated with the largest signal to noise ratio value computed by one QR decomposition operation of the full channel matrix spatially and temporally combined. It performs the iterative channel scaling operation about the channel matrix and singular value decomposition about the channel scaled matrix. It is shown that the proposed antenna selection algorithm leads to a substantial improvement in the error performance while keeping low-complexity, and obtains almost the same error performance as the exhaustive search-based optimal antenna selection algorithm.

The outage performance of a multiuser two-way amplify-and-forward (AF) relaying network, where N-th best selection scheme with the consideration to the feedback delay, is investigated. Specifically, the new closed-form expressions for cumulative distribution function (CDF) and outage probability (OP) are presented over time varying Rayleigh-fading channels. Furthermore, simple approximate OP is derived assessing the high signal-to-noise-ratio (SNR), which identifies the diversity behavior. Numerical results show excellent agreement with theoretical results.

In this paper, a two-layer Multiple Kernel Learning (MKL) scheme for speaker-independent speech emotion recognition is presented. In the first layer, MKL is used for feature selection. The training samples are separated into n groups according to some rules. All groups are used for feature selection to obtain n sparse feature subsets. The intersection and the union of all feature subsets are the result of our feature selection methods. In the second layer, MKL is used again for speech emotion classification with the selected features. In order to evaluate the effectiveness of our proposed two-layer MKL scheme, we compare it with state-of-the-art results. It is shown that our scheme results in large gain in performance. Furthermore, another experiment is carried out to compare our feature selection method with other popular ones. And the result proves the effectiveness of our feature selection method.

This paper proposes an opportunistic feedback and user selection method for a multiuser two-way relay channel (MU-TWRC) in a time-varying environments where a base station (BS) and a selected mobile station (MS), one of K moving MSs, exchange messages during two time slots via an amplify-and-forward relay station. Specifically, under the assumption of perfect channel reciprocity, we analyze the outage probabilities of several channel feedback scenarios, including the proposed scheme. Based on the analysis, the transmission rates are optimized and the optimal user selection method is proposed to maximize the expected sum throughput. The simulation results indicate that, with opportunistic feedback, the performance can be significantly improved compared to that without feedback. Moreover, the performance is nearly identical to that with full feedback, and close to the case of perfect channel state information at BS for low mobility MSs.

Spectrum sensing is one of the main functions in cognitive radio networks. To improve the sensing performance and increase spectrum efficiency, a number of cooperative spectrum sensing methods have been proposed. However, most of these methods focused on a single-channel environment. In this letter, we present a novel cooperative spectrum sensing method based on cooperator selection in a multi-channel cognitive radio network. Using reinforcement learning, a cognitive radio user can select reliable and robust cooperators, without any a priori knowledge. Using the proposed method, a cognitive radio user can achieve better sensing capability and overcome performance degradation problems due to malicious users or erratic user behavior. Numerical results show that the proposed method can achieve excellent performance.

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

A mixed storage-type design using flip-flops and latches (FF/latch-based design) has advantages on such as area and power compared to single storage-type design (only flip-flops or latches). Considering FF/latch-based design at high-level synthesis is necessary, because resource binding process significantly affects the quality of resulting circuits. One of the fundamental aspects in FF/latch-based design is that different resource binding solutions could lead to the different numbers of latch-replacable registers. Therefore, as a first step, this paper addresses a datapath design problem in which resource binding and selecting storage-types of registers are simultaneously optimized for datapath area minimization (i.e., latch replacement maximization). An efficient algorithm based on the compatibility path decomposition and an integer linear programming-based exact approach are presented. Experiments confirm the effectiveness of the proposed approaches.