One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.

Accurate counting of cereals crops, e.g., maize, rice, sorghum, and wheat, is crucial for estimating grain production and ensuring food security.

Comments and CorrectionsConvergence Analysis on Trace Ratio Linear Discriminant Analysis Algorithms.

Linear discriminant analysis (LDA) may yield an inexact solution by transforming a trace ratio problem into a corresponding ratio trace problem. Most recently, optimal dimensionality LDA (ODLDA) and trace ratio LDA (TRLDA) have been developed to overcome this problem. As one of the greatest contributions, the two methods design efficient iterative algorithms to derive an optimal solution.

A Segmentation-Guided Deep Learning Framework for Leaf Counting.

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e.

Multiview Learning With Robust Double-Sided Twin SVM.

Multiview learning (MVL), which enhances the learners' performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support vector machine (GEPSVM). However, this approach cannot guarantee good classification performance and robustness yet.

Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics.

Recently, there are many works on discriminant analysis, which promote the robustness of models against outliers by using L- or L-norm as the distance metric. However, both of their robustness and discriminant power are limited. In this article, we present a new robust discriminant subspace (RDS) learning method for feature extraction, with an objective function formulated in a different form.

PreLnc: An Accurate Tool for Predicting lncRNAs Based on Multiple Features.

Accumulating evidence indicates that long non-coding RNAs (lncRNAs) have certain similarities with messenger RNAs (mRNAs) and are associated with numerous important biological processes, thereby demanding methods to distinguish them. Based on machine learning algorithms, a variety of methods are developed to identify lncRNAs, providing significant basic data support for subsequent studies. However, many tools lack certain scalability, versatility and balance, and some tools rely on genome sequence and annotation.

Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization.

Multiview Generalized Eigenvalue Proximal Support Vector Machine (MvGEPSVM) is an effective method for multiview data classification proposed recently. However, it ignores discriminations between different views and the agreement of the same view. Moreover, there is no robustness guarantee.

Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification.

The graph-based semisupervised label propagation (LP) algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, the available methods typically calculate the weights and estimate the labels in the original input space, which typically contains noise and corruption.

Nonpeaked Discriminant Analysis for Data Representation.

Of late, there are many studies on the robust discriminant analysis, which adopt L-norm as the distance metric, but their results are not robust enough to gain universal acceptance. To overcome this problem, the authors of this article present a nonpeaked discriminant analysis (NPDA) technique, in which cutting L-norm is adopted as the distance metric. As this kind of norm can better eliminate heavy outliers in learning models, the proposed algorithm is expected to be stronger in performing feature extraction tasks for data representation than the existing robust discriminant analysis techniques, which are based on the L-norm distance metric.

Corrigendum to "Analysis of the Complete Mitochondrial Genome Sequence of the Diploid Cotton by Comparative Genomics Approaches".

[This corrects the article DOI: 10.1155/2016/5040598.].

Robust auto-weighted projective low-rank and sparse recovery for visual representation.

Most existing low-rank and sparse representation models cannot preserve the local manifold structures of samples adaptively, or separate the locality preservation from the coding process, which may result in the decreased performance. In this paper, we propose an inductive Robust Auto-weighted Low-Rank and Sparse Representation (RALSR) framework by joint feature embedding for the salient feature extraction of high-dimensional data. Technically, the model of our RALSR seamlessly integrates the joint low-rank and sparse recovery with robust salient feature extraction.

Flexible non-greedy discriminant subspace feature extraction.

Recently, L-norm-based non-greedy linear discriminant analysis (NLDA-L) for feature extraction has been shown to be effective for dimensionality reduction, which obtains projection vectors by a non-greedy algorithm. However, it usually acquires unsatisfactory performances due to the utilization of L-norm distance measurement. Therefore, in this brief paper, we propose a flexible non-greedy discriminant subspace feature extraction method, which is an extension of NLDA-L by maximizing the ratio of L-norm inter-class dispersion to intra-class dispersion.

Robust capped L1-norm twin support vector machine.

Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers. In this paper, we propose a new robust capped L1-norm twin support vector machine (CTWSVM), which sustains the advantages of TWSVM and promotes the robustness in solving a binary classification problem with outliers.

Recursive Discriminative Subspace Learning With l -Norm Distance Constraint.

In feature learning tasks, one of the most enormous challenges is to generate an efficient discriminative subspace. In this paper, we propose a novel subspace learning method, named recursive discriminative subspace learning with an l -norm distance constraint (RDSL). RDSL can robustly extract features from the contaminated images and learn a discriminative subspace.

Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation.

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data.

Adaptive non-negative projective semi-supervised learning for inductive classification.

We discuss the inductive classification problem by proposing a joint framework termed Adaptive Non-negative Projective Semi-Supervised Learning (ANP-SSL). Specifically, ANP-SSL integrates the adaptive inductive label propagation, adaptive reconstruction weights learning and the neighborhood preserving projective nonnegative matrix factorization (PNMF) explicitly. To make the label prediction results more accurate, ANP-SSL incorporates the semi-supervised data representation and classification errors into regular PNMF for minimization, which can enable our ANP-SSL to perform the adaptive weights learning and label propagation over the spatially local and part-based data representations, which differs from most existing work that usually assign weights and predict labels based on the original data that often has noise and corruptions.

Underlying Connections Between Algorithms For Nongreedy LDA-L1.

To solve the essential objective of LDA-L1, NLDA-L1 proposes a nongreedy algorithm by constructing an auxiliary function. In this correspondence, we show that essentially, this algorithm directly solves the objective using a gradient ascending procedure, meaning that the auxiliary function may be not necessary. Then, we further show that NLDA-L1 is a special case of ILDA-L1, which applies the same iterative procedure of ILDA-L1.

GEsture: an online hand-drawing tool for gene expression pattern search.

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods.

Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis.

Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. However, these methods have no guarantee of obtaining a satisfactory-enough performance due to the insufficient robustness of L1-norm measure. To mitigate this problem, inspired by recent works on Lp-norm based learning, this paper proposes a new discriminant method, called Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis (FLDA-Lsp).

Organellar genome assembly methods and comparative analysis of horticultural plants.

Although organellar genomes (including chloroplast and mitochondrial genomes) are smaller than nuclear genomes in size and gene number, organellar genomes are very important for the investigation of plant evolution and molecular ecology mechanisms. Few studies have focused on the organellar genomes of horticultural plants. Approximately 1193 chloroplast genomes and 199 mitochondrial genomes of land plants are available in the National Center for Biotechnology Information (NCBI), of which only 39 are from horticultural plants.

L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector $k$ -Plane Clustering.

Twin support vector clustering (TWSVC) is a recently proposed powerful k-plane clustering method. It, however, is prone to outliers due to the utilization of squared L2-norm distance. Besides, TWSVC is computationally expensive, attributing to the need of solving a series of constrained quadratic programming problems (CQPPs) in learning each clustering plane.

The complete chloroplast genome sequence of an economic plant .

is a paramount economic plant with great economic value. The complete chloroplast (cp) genome is 154,751 bp in length, including a large single copy (LSC) region of 84,850 bp, a small single copy (SSC) region of 18,131 bp and a pair of inverted repeats (IRs) of 25,885bp. This cp genome contains 131 genes, comprising of 86 protein-coding genes, 37 tRNAs and 8 rRNAs.

Assembly and comparative analysis of complete mitochondrial genome sequence of an economic plant .

Willow is a widely used dioecious woody plant of family in China. Due to their high biomass yields, willows are promising sources for bioenergy crops. In this study, we assembled the complete mitochondrial (mt) genome sequence of with the length of 644,437 bp using Roche-454 GS FLX Titanium sequencing technologies.

Analysis of the Complete Mitochondrial Genome Sequence of the Diploid Cotton by Comparative Genomics Approaches.

Cotton is one of the most important economic crops and the primary source of natural fiber and is an important protein source for animal feed. The complete nuclear and chloroplast (cp) genome sequences of are already available but not mitochondria. Here, we assembled the complete mitochondrial (mt) DNA sequence of into a circular genome of length of 676,078 bp and performed comparative analyses with other higher plants.

Genome-wide identification and characterization of WRKY gene family in Salix suchowensis.

WRKY proteins are the zinc finger transcription factors that were first identified in plants. They can specifically interact with the W-box, which can be found in the promoter region of a large number of plant target genes, to regulate the expressions of downstream target genes. They also participate in diverse physiological and growing processes in plants.