Summary This paper describes a new automated classification method for Wuyi rock tealeaves based on the best penalty parameter selection for the support vector machine with RBF (Radial Basis Function) kernel. A total of 3590 fresh tealeaf images of the representative Rou Gui and Shui Hsien varieties of Wuyi rock tea are collected in their natural habitat. Fourteen image features are extracted in terms of the leaf shape and texture. The automatic selection method is used to find the optimum RBF kernel parameter sigma, which is then applied to design an automatic parameter selection method to screen the best penalty parameter C for the classification of Wuyi rock tealeaves. In this study, the SVM classifier is used for the automated classification and recognition of the 14 image features. The contribution of the various features to the recognition rate of fresh tealeaves is evaluated to identify the key features for the classification and recognition of fresh Wuyi rock tealeaf images. The experimental results show that the proposed method improves the recognition rate of fresh tealeaves to 91.00%.

Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with a Gaussian radial basis function (RBF) kernel is a nonlinear classifier [7]-[8]. It means that if the corresponding feature subset has the largest nonlinear separability with a RBF kernel, then the corresponding SVM can have a better classification performance. Hence, in this study, feature subsets are randomly selected without replacement from a kernel (nonlinear) feature importance [9] determined by the largest nonlinear between-class separability and the smallest nonlinear within-class separability with respect to the RBF kernel. The results from the experiments showed that the proposed method can improve the classification performance using only a few features. In addition, the rate of classification accuracy is higher than those based on the feature subsets determined by the descending order of feature importance.

Shared last-level cache (LLC) management is a critical design issue for heterogeneous multi-cores. In this paper, we observe two major challenges: the contribution of LLC latency to overall performance varies among applications/cores and also across time; overlooking the off-chip latency factor often leads to adverse effects on overall performance. Hence, we propose a Latency Sensitivity-based Cache Partitioning (LSP) framework, including a lightweight runtime mechanism to quantify the latency-sensitivity and a new cost function to guide the LLC partitioning. Results show that LSP improves the overall throughput by 8% on average (27% at most), compared with the state-of-the-art partitioning mechanism, TAP.

NAND flash-based solid-state drives (SSDs), which can serve as the caches of hard disk drives, have gained popularity in large-scale, high-performance storage. A type of advanced error correction code for SSDs, low-density parity-check (LDPC), is required to mitigate a considerable number of errors in the raw data of NAND flash. However, LDPC imposes read performanceoverhead due to the complex decoding procedure of LDPC. In this paper, we propose an error-correcting cache (EC-Cache) that exploits “error locality”, a characteristic of NAND flash memory, to improve the read performance of SSDs. We use the term “error locality” to refer to the property that the majority of errors in reads to the same flash page appear at the same positions until thepage is erased. By caching detected errors, we can correct a significant portion of errors in the requested flash page prior to the LDPC decoding process. This design significantly reduces LDPC decoding overhead because the latency of LDPC is correlated with thenumber of errors in the input data. We conduct experiments, including flash characterization, LDPC simulation, and SSD simulation,to evaluate EC-Cache. The experimental results demonstrate that EC-Cache can improve the read performance of LDPC-based SSDs by up to$2.6\times$.

Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification and other applications. In 2012, an automatic kernel parameter selection (APS) based on kernel-based within- and between-class separability measures were proposed. Moreover, the application for determining the kernel parameters of the full bandwidth RBF (FRBF) kernel was proposed. In this study, the bandwidths of the FRBF kernel were considered as the weights of the features when the feature values are rescaled by computing the z-scores. Experimental results on the Indian Pine Site dataset showed that the SVM based on the proposed feature subset outperforms than the SVMs based on the RBF kernel and FRBF kernel.

For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that they can improve the classification performance. However, for GDA and KNWFE, it is hard to find the suitable kernel parameters. Hence, although they have been published about 14 or 6 years, respectively, researchers rarely implement them for dealing with hyperspectral image classification problem. An automatic kernel parameter selection method (APS) was proposed to predetermine the appropriate radial basis function (RBF) kernel for support vector machine (SVM) and GDA. In this study, APS was applied to find the suitable RBF kernel function for KNWFE. From the experiment results on PAVIA data set, the classification performance of KNWFE still outperforms those of GDA [10] and SVM [10]. The most important of this research, the kernel parameters of GDA and KNWFE based on RBF kernel can be “automatically” determined and the researcher can implement them directly without tuning the kernel parameter.

Low-density parity-check (LDPC) is widely accepted as the baseline error-correction codes offering strong error-correcting capability for future NAND flash-based SSDs. However, LDPC incurs read performance overhead because of its complex decoding procedure. To mitigate such overhead, we propose the error-correcting cache (EC-Cache) that exploits the "error locality" of NAND flash. Error locality means that the majority of errors in reads to the same NAND flash page appear in the same positions until the page is erased. By caching detected errors, EC-Cache can correct a significant portion of errors present in a requested flash page before the associated LDPC decoding process begins. EC-Cache can greatly speed up LDPC decoding because LDPC's latency is directly correlated to the number of errors present in the input data. Experimental results show that EC-Cache achieves up to 2.6× SSD read performance gain.

Feature extraction can overcome the Hughes phenomenon for hyperspectral image classification. Linear discriminant analysis (LDA) is a basic supervised feature extraction method. However, LDA only cannot extract features more than number of classes. The semi-supervised local discriminant analysis (SELD) was proposed to solve the above problem by combing the scatter matrices of LDA and the neighborhood preserving embedding (NPE). Some unlabeled samples were used to form the scatter matrices of NPE. It can preserve the local geometric property according to the used unlabeled samples. Moreover, the between-class scatter matrix of SELD is nonsingular, and more features can be extracted by applying SELD. However, in SELD, the unlabeled sample were randomly selected. The local geometric property around the training samples cannot be preserved due to the randomly selection. In this study, the concept of the Voronoi diagram is used to determine the regions according to the training samples, and the unlabeled samples are chosen in the regions based on the nearest neighbors. Experimental results on the Indian Pine Site dataset show that the proposed method outperforms SELD with less number of unlabeled samples on the small sample size problem.

The support vector machine (SVM) is widely used in hyperspectral image classification due to the robust to the Hughes phenomenon. However, the performance of SVM highly depends on the kernel parameter selection. Hence, it is hard to apply the SVM based on the kernel with lots of parameters such as the full bandwidth RBF (FRBF) kernel whose number of parameters is equal to the number of features. In our previous study, an automatic kernel parameter selection method (APS) was proposed for the normalized kernel function. The proper kernel parameters are the minimizer of the optimization problem based on the proposed kernel-based class separability measure. In this study, we apply the APS to find the best kernel parameters of the FRBF kernel. Experimental results on the Indian Pine Site dataset show that the SVM based on the FRBF kernel with proper kernel parameters outperforms than the SVM based on the RBF kernel on the small sample size problem.

Most studies showed that most hyperspectral image classification encountered the Hughes phenomenon due to the redundant features, especially in the small sample size problem. Feature extraction method such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE) is a preprocessing step before classification and used to combine and reduce the original features into a new feature space based on the between-class and with-class separability. Then, the classifier such as the nonlinear support vector machine (SVM) is trained and classifies the unknown samples. However, the separability measurement of LDA and NWFE is for the original space not the kernel-induced feature space. In this study, a kernel-based feature extraction method is proposed. The corresponding transformation matrix for dimension reduction is based on the class separability in the kernel-induced feature space which was proposed in our previous study. Experimental results on the Indian Pine Site dataset show that the proposed method improves the classification performance of the SVM on the small sample size problem.

Adopting SSDs as caches for HDD arrays has gained popularity in datacenters because SSDs are superior in handling random reads that HDDs cannot efficiently deal with. Two types of flash memory cells are available for building SSD caches, single-level cells (SLC) and multi-level cells (MLC). MLC is more appealing than SLC because it can achieve higher cache capacity at the same cost. However, we see a critical issue for SSD caches to adopt MLC NAND flash: the endurance of modern MLC NAND flash is too low to sustain datacenter workloads. In this paper, we propose DuraCache that addresses the durability issue of SSD caches. DuraCache exploits the fact that SSD caches are write-through caches in datacenters. Therefore, uncorrectable errors in SSD caches can be handled like cache misses which bring in correct data from HDD arrays. In addition, DuraCache gradually allocates more ECC parities associated with data when NAND flash reaches wearout thresholds. This allows SSD caches to continue operating by sacrificing available capacity. We conduct empirical experiments and demonstrate that DuraCache enables MLC SSD caches to achieve 4.1 years of service life assuming a TPC-C workload.

Recent studies show that hyperspectral image classification techniques that use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. Using a spatial-contextual term, this study modifies the decision function and constraints of a support vector machine (SVM) and proposes two kinds of spatial-contextual SVMs for hyperspectral image classification. One machine, which is based on the concept of Markov random fields (MRFs), uses the spatial information in the original space (SCSVM). The other machine uses the spatial information in the feature space (SCSVMF), i.e., the nearest neighbors in the feature space. The SCSVM is better able to classify pixels of different class labels with similar spectral values and deal with data that have no clear numerical interpretation. To evaluate the effectiveness of SCSVM, the experiments in this study compare the performances of other classifiers: an SVM, a context-sensitive semisupervised SVM, a maximum likelihood (ML) classifier, a Bayesian contextual classifier based on MRFs (ML_MRF), and nearest neighbor classifier. Experimental results show that the proposed method achieves good classification performance on famous hyperspectral images (the Indian Pine site (IPS) and the Washington, DC mall data sets). The overall classification accuracy of the hyperspectral image of the IPS data set with 16 classes is 95.5%. The kappa accuracy is up to 94.9%, and the average accuracy of each class is up to 94.2%.

Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, a semisupervised feature extraction method which is based on the scatter matrices of the fuzzy-type LDA and uses the semi-information is proposed. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.

In remote sensing researches, the curse of dimensionality is one greatly difficult classification problem. Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), can alleviate small sample size and high dimensionality concern and obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. A dynamic subspace method (DSM) was proposed for constructing component classifiers with adaptive subspaces to adjust the shortcomings of RSM based on re substitution accuracy by applying each classifier. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. The objective of this research is to develop a novel ensemble technique based on support vector machines (SVMs) via the optimal kernel method, and propose a novel subspace selection mechanism, named the kernel-based dynamic subspace method (KDSM), to improve DSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Experimental results show a sound performance of classification on the famous hyperspectral images, Washington DC Mall.

Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.

Research has shown fuzzy c-means (FCM) clustering to be a powerful tool to partition samples into different categories. However, the objective function of FCM is based only on the sum of distances of samples to their cluster centers, which is equal to the trace of the within-cluster scatter matrix. In this study, we propose a clustering algorithm based on both within- and between-cluster scatter matrices, extended from linear discriminant analysis (LDA), and its application to an unsupervised feature extraction (FE). Our proposed methods comprise between- and within-cluster scatter matrices modified from the between- and within-class scatter matrices of LDA. The scatter matrices of LDA are special cases of our proposed unsupervised scatter matrices. The results of experiments on both synthetic and real data show that the proposed clustering algorithm can generate similar or better clustering results than 11 popular clustering algorithms: K-means, K-medoid, FCM, the Gustafson-Kessel, Gath-Geva, possibilistic c-means (PCM), fuzzy PCM, possibilistic FCM, fuzzy compactness and separation, a fuzzy clustering algorithm based on a fuzzy treatment of finite mixtures of multivariate Student's t distributions algorithms, and a fuzzy mixture of the Student's t factor analyzers model. The results also show that the proposed FE outperforms principal component analysis and independent component analysis.

In the kernel methods, it is very important to choose a proper kernel function to avoid overlapping data. Based this fact, in this paper we mainly utilize a unified kernel optimization framework on the hyperspectral image classification to augment the margin between different classes, and under the kernel optimization framework, to employ the Fisher discriminant criteria formulated in a pairwise manner as the objective functions to optimize the kernel function in Kernel-based nonparametric weighted feature extraction. The experimental results display the superiority of the optimizing kernel function over the RBF kernel function with 5-fold cross-validation method, especially, in the small sample size problem.

Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the RBF kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.

In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, and some results show that kernel-based classifiers have satisfying performances. Many studies about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. However, NWFE is still based on linear transformation. In this paper, the kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation, and the experimental results show that KNWFE outperforms NWFE, decision-boundary feature extraction, independent component analysis, kernel-based principal component analysis, and generalized discriminant analysis.

The k-nearest neighbor classifier is a simple and appealing approach to classification problems. It expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensional situation. Using a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. A technique that computes a locally flexible metric by means of the decision boundaries of support vector machines (SVMs) is proposed. Then the modified neighborhoods can be shrunk in directions orthogonal to these decision boundaries and elongated parallel to the boundaries. Thereafter, any neighborhood-based classifier can use the modified neighborhoods.

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many studies show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. The detection of class boundaries is an important part in NWFE and the weighted mean was defined for this purpose. In this paper, a kernel-based feature extraction is proposed based on a new class boundary detection mechanism. The soft-margin support vector machine (SVM) binary classifier and the support vector domain description (SVDD) are applied to detect the boundaries between two classes and one class, respectively. The results of real data experiments show that the proposed method outperforms original NWFE.

Kernel nonparametric feature extraction (KNWFE) is a power tool for hyperspectral image classification in feature space. However, the performances of KNWFE largely depend on the choices of kernels. In this paper, a method is proposed for optimizing kernel function using the weighted means of KNWFE and the Fisher scalar with respect to KNWFE. The idea is to obtain a feature space with the largest nonparametric separability of training samples between classes by employing the parameters of a conformal transformation of a basic kernel. Experimental results show that the proposed method has a remarkable improvement of KNWFE for real data for two-classe problem.

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Some studies show that nonparametric weighted feature extraction (NWFE; Kuo and Landgrebe, 2004) is a powerful tool to extract hyperspectral image features for classification. Recently, some studies also show that kernel-based methods are computationally efficient, robust and stable for pattern analysis. In this study, a kernel-based NWFE (KNWFE) is proposed for hyperspectral image classification. In this paper, we show that KNWFE is a generalization of original NWFE.