Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive new cost functions for spectral clustering based on measures of error between a given partition and a solutio...

Inter-session network coding is well known for its ability to spectral efficiency and endto-end throughput in wireless multi-hop networks by up to four fold on some scenarios compared to standard routing. However, a variety of inter-session network coding implementations have consistently shown a decrease in throughput when operating at high traffic loads and significantly below the values expe...

A weighted graph is used as an underlying structure of many algorithms like semisupervised learning and spectral clustering. If the edge weights are determined by a single similarity measure, then it hard if not impossible to capture all relevant aspects of similarity when using a single similarity measure. In particular, in the case of visual object matching it is beneficial to integrate diffe...

In this paper, we present a framework for “opportunistic scheduling” that exploits the variation of the wireless channel conditions to improve spectrum efficiency. The objective of the scheduling schemes in this paper is to maximize the system performance in an opportunistic fashion, while satisfying various QoS requirements. We study three types of QoS requirements: the first is fairness in re...

In this paper, we consider a Cognitive Radio (CR) OFDMA-based network, in which the nodes detect available spectrum resources, and adopt a subset of accessible spectrum units (subcarriers). The idea of our approach to the efficient spectrum utilization is to allow each node to optimize the resources acquisition, where the competition of nodes for available common resources is observed. This can...

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value ...

Automatically generated databases of English paraphrases have the drawback that they return a single list of paraphrases for an input word or phrase. This means that all senses of polysemous words are grouped together, unlike WordNet which partitions different senses into separate synsets. We present a new method for clustering paraphrases by word sense, and apply it to the Paraphrase Database ...

We investigate how information leakage reduces computational entropy of a random variable X. Recall that HILL and metric computational entropy are parameterized by quality (how distinguishable is X from a variable Z that has true entropy) and quantity (how much true entropy is there in Z). We prove an intuitively natural result: conditioning on an event of probability p reduces the quality of m...

Recently, semi-supervised spectral clustering algorithms have been developing rapidly, which are proposed to improve the clustering performance. In this paper, we first review the current existing spectral clustering algorithms in an unified-framework and give a straightforward explanation about the spectral clustering algorithm. Then, we present a semi-supervised method to improve the clusteri...

Spectral clustering refers to a class of recent techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this chapter, we introduce the main concepts and algorithms together with recent advances in learning the similarity matrix ...