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We present an application of a Multiple Instance Learning (MIL) approach to image classification. In particular we focus on a recent MIL method for binary classification where the objective is to discriminate between positive and negative sets of points. Such sets are called bags and the points inside the bags are called instances. In the case of two classes of instances (positive and negative), a...
We consider the semi-supervised dimension reduction problem: given a high dimensional dataset with a small number of labeled data and huge number of unlabeled data, the goal is to find the low-dimensional embedding that yields good classification results. Most of the previous algorithms for this task are linkage-based algorithms. They try to enforce the must-link and cannot-link constraints in dimension...
The existence of non-line-of-sight (NLOS) errors will considerably degrade the localization accuracy. Therefore, the NLOS node localization is investigated. In this article, we propose a NLOS node localization algorithm that utilizes the firefly algorithm. The objective function is established by the probability of propagation of NLOS and LOS according to the approximate maximum likelihood method...
As the blooming development of data mining in social computing systems (e.g., crowdsourcing system), statistical inference from crowdsourced data severs as a powerful tool to provide diversified services. To support critical applications (e.g., recommendation), in this paper, we shall focus on the collaborative ranking problems and construct a system of which the input is crowdsourced pairwise comparisons...
Although many articles have shown that the control performance of interval type-2 fuzzy logic controllers (IT2 FLCs) is better than type-1 FLCs, the computational cost of it is high, which makes it hard to develop in the real world. Our previous research has recommended six TR approaches for their efficiency, but which one has the best performance is still an unknown problem. In the paper, IT2 FLCs...
This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are...
We consider the following basic problem: given an n-variate degree-d homogeneous polynomial f with real coefficients, compute a unit vector x in R{\string^}n that maximizes abs(f(x)). Besides its fundamental nature, this problem arises in diverse contexts ranging from tensor and operator norms to graph expansion to quantum information theory. The homogeneous degree-2 case is efficiently solvable as...
We give a quantum algorithm for solving semidefinite programs (SDPs). It has worst-case running time n^{\frac{1}{2}} m^{\frac{1}{2}} s^2 \poly(\log(n), \log(m), R, r, 1/δ), with n and s the dimension and row-sparsity of the input matrices, respectively, m the number of constraints, δ the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual...
We give a nearly linear-time randomized approximation scheme for the Held-Karp bound [22] for Metric-TSP. Formally, given an undirected edge-weighted graph G = (V,E) on m edges and ε 0, the algorithm outputs in O(m log^4 n/ε^2) time, with high probability, a (1 + ε)-approximation to the Held-Karp bound on the Metric-TSP instance induced by the shortest path metric...
In this paper, we propose a subspace method for solving unconstrained optimization problems without derivatives. In each iteration, we construct a subspace, which enables a large-scale unconstrained optimization problem to be transformed into a lower dimensional subproblem. This paper describes how to use polynomial interpolation model to capture the information of function and its derivatives, and...
Wireless multicast is a key enabling technology for efficient distribution of high-rate multimedia services in future wireless communication systems. When there are a massive number of users in the system, it is impractical or inefficient to serve all users with a single multicast beamformer and within a single channel. We therefore study the joint multicast beamforming and user scheduling problem,...
Deep multi-layer neural networks are generally trained using variants of the gradient descent based algorithm. However, this kind of algorithms usually encounter a series of shortcomings, such as low training efficiency, local minimum, difficult control parameter tuning, and gradient vanishing or exploding. Besides, for a specific application, how to design the structure of the network, that is, how...
Nowadays, Rate-Distortion Optimization (RDO) is commonly used in hybrid video coding to maximize coding efficiency. Usually, the rate distortion tradeoff is explicitly computed in offline encoder implementations whereas R(D) model are used in live encoders to select the best decisions at a lower computational cost. For sake of simplicity, this (mathematical) modelling is often performed for each coding...
Software Defined Networking (SDN) separates the control plane from the data plane to ease network management and provide flexibility in packet routing. The control plane interacts with the data plane through the forwarding tables, usually including a flow table and a group table, at each switch. Due to high cost and power consumption of Ternary Content Addressable Memory (TCAM), commodity switches...
We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Followthe- Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by...
We present a general framework for stochastic online maximization problems with combinatorial feasibility constraints. The framework establishes prophet inequalities by constructing price-based online approximation algorithms, a natural extension of threshold algorithms for settings beyond binary selection. Our analysis takes the form of an extension theorem: we derive sufficient conditions on prices...
This paper studies routing performance loss due to traffic split ratio granularity constraints. For any given fineness of traffic splitting capability, we provide upper bounds on the loss of performance. Based on tight convex relaxation, we further develop an efficient approximation algorithm to compute a good routing solution that satisfies given constraints on traffic splitting. The results can...
Orthogonal frequency division multiplexing access (OFDMA) inherently suffers from large spectral sidelobes, which will lead to interference between users and can not be ignored. In this paper, in order to suppress spectral sidelobes, we propose a novel windowing scheme. The derivation of the optimum window function is based on the solution of an optimization problem. It aims to minimize in-band-out-of-subband...
We address the problem of epipolar geometry using the motion of silhouettes. Such methods match epipolar lines or frontier points across views, which are then used as the set of putative correspondences. We introduce an approach that improves by two orders of magnitude the performance over state-of-the-art methods, by significantly reducing the number of outliers in the putative matching. We model...
Fuelled by the proliferation of smartphones, wireless traffic has experienced huge growth, which will continue with the emergence of ultra-broadband 5G applications, and exacerbate the capacity strain in cellular networks. Deployment of pico access points, reducing cell sizes and allowing more efficient reuse of limited radio spectrum, provides a powerful approach to cope with traffic hot spots and...
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