Data collected from IoT devices can be encrypted and analysed by computer resources at the network edge in your nearby cloud to alleviate congestion load infrastructure, whilst different applications indeed run clouds reduce time among both users such as mobile user devices) clouds. Taking into account geographical temporal characteristics of working loads each application, allocation across fo...

Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribu...

We present a hierarchical maximum-margin clustering method for unsupervised data analysis. Our method extends beyond flat maximummargin clustering, and performs clustering recursively in a top-down manner. We propose an effective greedy splitting criteria for selecting which cluster to split next, and employ regularizers that enforce feature sharing/competition for capturing data semantics. Exp...

We introduce Gaussian Margin Machines (GMMs), which maintain a Gaussian distribution over weight vectors for binary classification. The learning algorithm for these machines seeks the least informative distribution that will classify the training data correctly with high probability. One formulation can be expressed as a convex constrained optimization problem whose solution can be represented ...

In this paper, we propose a maximum-margin framework for classification using Nonnegative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization stages are separated, we incorporate the maximum margin constraints within the NMF formulation, i.e we solve for a base matrix that maximizes the margin of the classifier in the low dimensional featur...

In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predicted from the input, so it is also critical to have a predictable label encodi...

Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on extensions of generative models such as k-means or Switching Linear Dynamical Systems (SLDS) often lead to intractable inference and lack a mechanism for feature selection, critical when dealing with high dimensional da...

In bulk electric power transfer capability computations, the transmission reliability margin accounts for uncertainties related to the transmission system conditions, contingencies, and parameter values. We propose a formula which quantifies transmission reliability margin based on transfer capability sensitivities and a probabilistic characterization of the various uncertainties. The formula i...

Maximum margin clustering (MMC), which borrows the large margin heuristic from support vector machine (SVM), has achieved more accurate results than traditional clustering methods. The intuition is that, for a good clustering, when labels are assigned to different clusters, SVM can achieve a large minimum margin on this data. Recent studies, however, disclosed that maximizing the minimum margin...

Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first K principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank K projections. Thus, PCA pro...