Mingmin Chi

A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images

This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions

Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines (S3VMs). In particular, we analyzed the critical problem of the nonconvexity of the cost function associated with the learning phase of S3VMs by considering different (S3VMs) techniques that solve optimization directly in the primal formulation of the objective function. As the nonconvex cost function can be characterized by many local minima, different optimization techniques may lead to different classification results. Here, we present two implementations, which are based on different rationales and optimization methods. The presented techniques are compared with S3VMs implemented in the dual formulation in the context of classification of real hyperspectral remote sensing images. Experimental results point out the effectiveness of the techniques based on the optimization of the primal formulation, which provided higher accuracy and better generalization ability than the S3VMs optimized in the dual formulation

A continuation method for semi-supervised SVMs

Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.

From web directories to ontologies: natural language processing challenges

Hierarchical classifications are used pervasively by humans as a means to organize their data and knowledge about the world. One of their main advantages is that natural language labels, used to describe their contents, are easily understood by human users. However, at the same time, this is also one of their main disadvantages as these same labels are ambiguous and very hard to be reasoned about by software agents. This fact creates an insuperable hindrance for classifications to being embedded in the Semantic Web infrastructure. This paper presents an approach to converting classifications into lightweight ontologies, and it makes the following contributions: (i) it identifies the main NLP problems related to the conversion process and shows how they are different from the classical problems of NLP; (ii) it proposes heuristic solutions to these problems, which are especially effective in this domain; and (iii) it evaluates the proposed solutions by testing them on DMoz data.

A semilabeled-sample-driven bagging technique for ill-posed classification problems

In this letter, a semilabeled-sample-driven bootstrap aggregating (bagging) technique based on a co-inference (inductive and transductive) framework is proposed for addressing ill-posed classification problems. The novelties of the proposed technique lie in: 1) the definition of a general classification strategy for ill-posed problems by the joint use of training and semilabeled samples (i.e., original unlabeled samples labeled by the classification process); and 2) the design of an effective bagging method (driven by semilabeled samples) for a proper exploitation of different classifiers based on bootstrapped hybrid training sets. Although the proposed technique is general and can be applied to any classification algorithm, in this letter multilayer perceptron neural networks (MLPs) are used to develop the basic classifier of the proposed architecture. In this context, a novel cost function for the training of MLPs is defined, which properly considers the contribution of semilabeled samples in the learning of each member of the ensemble. The experimental results, which are obtained on different ill-posed classification problems, confirm the effectiveness of the proposed technique.

Support cluster machine

For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.

Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.

An ensemble-driven k-NN approach to ill-posed classification problems

This paper addresses the supervised classification of remote-sensing images in problems characterized by relatively small-size training sets with respect to the input feature space and the number of classifier parameters (ill-posed classification problems). An ensemble-driven approach based on the k-nearest neighbor (k-NN) classification technique is proposed. This approach effectively exploits semilabeled samples (i.e., original unlabeled samples labeled by the classification process) to increase the accuracy of the classification process. Experimental results obtained on ill-posed classification problems confirm the effectiveness of the proposed approach, which significantly increases both the accuracy and the reliability of classification maps. ion maps.

Spectral Derivative Features for Classification of Hyperspectral Remote Sensing Images: Experimental Evaluation

Derivatives of spectral reflectance signatures can capture salient features of different land-cover classes. Such information has been used for supervised classification of remote sensing data along with spectral reflectance. In the paper, we study how supervised classification of hyperspectral remote sensing data can benefit from the use of derivatives of spectral reflectance without the aid of other techniques, such as dimensionality reduction and data fusion. An empirical conclusion is given based on a large amount of experimental evaluations carried out on three real hyperspectral remote sensing data sets. The experimental results show that when a training data set is of a small size or the quality of the data is poor, the use of additional first order derivatives can significantly improve classification accuracies along with original spectral features when using classifiers which can avoid the “curse of dimensionality,” such as the SVM algorithm.

Cluster-Based Ensemble Classification for Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multi-spectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides, the training samples may not represent the real distribution of the whole space. To attack the quality problems of training samples, we proposed a Cluster-based ENsemble Algorithm (CENA) for the classification of hyperspectral remote sensing images. Data set collected from ROSIS university validates the effectiveness of the proposed approach.