10964754
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that the introduction of data editing is beneficial, and the learned hypotheses of Setred outperform those learned by the standard self-training algorithm.
16550368
SOM Based Image Segmentation
Image segmentation plays an important role in image retrieval system. In this paper, a method for segmenting images based on SOM neural network is proposed. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished with a SOM network. Then, the clustered blocks are merged to a specific number of regions. Experiments show that these regions could be regarded as segmentation results reserving some semantic means. This approach thus provides a feasible new solution for image segmentation which may be helpful in image retrieval.
14171756
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples are known. In this paper, we propose an adaptive kernel selection method for kernel principal component analysis, which can effectively learn the kernels when the class labels of the training examples are not available. By iteratively optimizing a novel criterion, the proposed method can achieve nonlinear feature extraction and unsupervised kernel learning simultaneously. Moreover, a non-iterative approximate algorithm is developed. The effectiveness of the proposed algorithms are validated on UCI datasets and the COIL-20 object recognition database.
16528148
Supervised nonlinear dimensionality reduction for visualization and classification
When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap, namely S-Isomap, is proposed. S-Isomap utilizes class information to guide the procedure of nonlinear dimensionality reduction. Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. The dissimilarity has several good properties which help to discover the true neighborhood of the data and, thus, makes S-Isomap a robust technique for both visualization and classification, especially for real-world problems. In the visualization experiments, S-Isomap is compared with Isomap, LLE, and WeightedIso. The results show that S-Isomap performs the best. In the classification experiments, S-Isomap is used as a preprocess of classification and compared with Isomap, WeightedIso, as well as some other well-established classification methods, including the K-nearest neighbor classifier, BP neural network, J4.8 decision tree, and SVM. The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
8244593
A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm
This paper presents a study on hierarchical surrogate-assisted evolutionary algorithm (HSAEA) using different global surrogate models for solving computationally expensive optimization problems. In particular, we consider the use of Gaussian process (GP) and polynomial regression (PR) methods for approximating the global fitness landscape in the surrogate-assisted evolutionary search. The global surrogate model serves to pre-screen the EA population for promising individuals. Subsequently, these potential individuals undergo a local search in the form of Lamarckian learning using online local surrogate models. Numerical results are presented on two multimodal benchmark test functions. The results obtained show that both PR-HSAEA and GP-HSAEA converge to good designs on a limited computational budget. Further, our study also shows that the GP model is suitable for global surrogate modeling in HSAEA if the evaluation function is very expensive in computations. On moderately expensive problems, the PR method may serve to generate better efficiency than using GP.
205343152
On queuing lengths in on-line switching
Queues that temporarily store fixed-length packets are ubiquitous in network switches. Scheduling algorithms that prevent packet-loss are always desirable. LONGEST-QUEUE-FIRST (LQF) is an on-line greedy algorithm widely exploited because of its simplicity and efficiency. In this paper, we give improved bounds on the competitive ratio of LQF in terms of the worst-case queuing length, parameterized with respect to the optimal queuing length of a clairvoyant adversary. This gives a better picture of LQF's performance under heavy traffic than the usual (unparameterized) competitive ratio. We also discuss randomization, and we conclude with some intriguing open problems regarding a two-dimensional generalization of the problem.