1730596
A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
4841642
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in boosting the image retrieval performance.
16045709
A General Model and Analysis of Physical Layer Capture in 802.11 Networks
While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.
206472390
Gradient match and side match fractal vector quantizers for images
In this paper, we propose gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs), which are two classes of finite state fractal vector quantizers (FSFVQs), for the image coding framework. In our previous work, we proposed the noniterative fractal block coding (FBC) technique to improve the decoding speed and the coding performance for conventional FBC techniques. To reduce the number of bits for denoting the fractal code of the range block, the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs) are employed to the noniterative FBC technique. Unlike ordinary vector quantizers, the super codebooks in the proposed GMFVQs and SMFVQs are generated from the affine-transformed domain blocks in the noniterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in the affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that 15%-20% of the bit rates in the noniterative FBC technique are saved by using the proposed GMFVQs.
8605759
Fine-grained contract negotiation for hierarchical software components
Component-based development addresses the complexity of large applications by building software systems from reusable software components. To support reuse and successfully combine units of software, the contractual approach turns out to be well-suited to specify and to verify components and their interactions. However, as contracts must support functional and extra-functional aspects, they are frequently challenged by fluctuations in extra-functional properties and dynamic reconfigurations of components. In this paper, we propose a negotiation model in which components have clearly identified roles and interact in order to automatically restore the validity of contracts. The negotiation model currently supports a concession-based negotiation policy and is well-suited to behavioral contracts based on executable assertions. This model is integrated into ConFract, a contracting system for the fractal hierarchical component model.
1945331
From Components to Autonomic Elements Using Negotiable Contracts
Autonomic computing aims at producing software systems that can manage themselves. As component-based development also partly addresses the complexity of large applications, we propose to combine the benefits of both approaches by using components equipped with negotiable contracts. These contracts specify the correct behavior of the components and play a central role in feedback control loops to enforce some autonomic features on components. In this paper, we present ConFract, a contract-based framework for hierarchical components in which contracts are runtime objects that are dynamically built from specifications, and automatically updated according to dynamic reconfigurations. Moreover, contracts clearly define the responsibilities (guarantor, beneficiaries) between their participating components which are exploited by some negotiations to automatically adapt components and contracts, and revalidate the system. The generic negotiation mechanism and an associated concession-based policy are presented.