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We address the problem of determining the number of signals correlated between two high-dimensional data sets with small sample support. In this setting, conventional techniques based on canonical correlation analysis (CCA) cannot be directly applied since the canonical correlations are significantly overestimated when computed from few samples. To overcome this problem, a principal component analysis...
Conventional pixel-domain block matching temporal (inter) prediction is suboptimal, since it ignores the underlying spatial correlation. Hence in our recent research we proposed transform domain temporal prediction (TDTP), wherein spatially decorrelated transform coefficients are individually predicted. Later we proposed extended block TDTP (EB-TDTP), which fully exploits spatial correlation around...
This paper proposes a new framework that performs spectrum sensing in soft decision based distributed detection systems while considering correlated sensors' readings. The main contribution is formulating the problem into a nonlinear integer programming problem for which the genetic algorithm is further applied to find a suboptimal solution. This framework is able to handle both soft and hard decisions...
The effort required for the development of a software system is predicted through the cost of software estimation. Completion of project within time and budget limits is required for accurate cost estimation. Effort and cost estimation can be done through various modes. A new hybrid algorithm which is a combination of concepts of Artificial Bee Colony (ABC) and Local search procedures is used here...
A wide variety of error tolerant applications supports the use of approximate circuits that achieve power savings by introducing small errors. This paper proposes a fast and novel algorithm for the design of such circuits with the goal of maximizing power savings, constrained by a fixed error budget, through an analytical expression to optimally select the number of bits to be approximated. This algorithm...
Eliminating timing violations using clock tree optimization (CTO) persist to be a tedious problem in ultra scaled technologies. State-of-the-art CTO techniques are based on predicting the final timing quality by specifying a set of delay adjustments in the form of delay adjustment points (DAPs). Next, the DAPs are realized to eliminate the timing violations. Unfortunately, it is difficult to realize...
Previous studies have demonstrated that the brain functional connectivity undergoes striking temporal dynamics. Modeling dynamically the brain functional connectivity has become not only an urgent and important work, but also a new direction for exploring brain functional research. For this motivation, a novel method for exploring functional brain dynamics based on Fisher linear discriminant (FLD)...
This paper proposes an approach of recommending micro-learning path based on improved ant colony optimization algorithm. Micro-learning is a new learning style, which can be used to support learning in short time because of its micro-learning units. Each micro-learning unit consists of a small knowledge unit that can be learned at fragmented time. Meanwhile, micro-learning is more flexible than other...
We introduce a new algorithm that maps multiple instance data using both positive and negative target concepts into a data representation suitable for standard classification. Multiple instance data are characterized by bags which are in turn characterized by a variable number of feature vectors or instances. Each bag has a known positive or negative label, but the labels of any given instances within...
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space. We learn the embedding by minimizing an objective function that has two terms: one due to intra-view correlations and another due to inter-view...
In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared...
The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard...
Adverse effects, such as voice change and fatigue, are prevalent in cancer treatment duration. These adverse effects have been significant burden for patients physically and emotionally. Predicting multiple adverse effects becomes important for patients and oncologists. In this paper, we formulate the prediction of multiple adverse effects in cancer treatment as a longitudinal multiple-output regression...
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co-existence of multiple types of heterogeneity. In this paper, we propose a HEterogeneous REpresentation learning model with structured Sparsity regularization (HERES) to learn from multiple types of heterogeneity. HERES aims to leverage two kinds of information to build a robust...
Nowadays, social network analysis receives big attention from academia, industries and governments. Some practical applications such as community detection and centrality in economic networks have become main issues in this research area. Community detection algorithm for complex network analysis is mainly accomplished by the Louvain Method that seeks to find communities by heuristically finding a...
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that are based on building and sampling a probability model. Copula theory provides methods that simplify the estimation of the probability model. To improve the efficiency of current copula based EDAs (CEDAs) new modifications of parallel CEDA were proposed. We investigated eight variants of island-based algorithms...
Efficient Global Optimization (EGO) is a well established iterative approach originally introduced to solve computationally expensive unconstrained optimization problems. EGO relies on an underlying Gaussian Process (GP) model and identifies an infill location for sampling that maximizes the expected improvement (EI) function. The infill point is evaluated which in turn is used to update the GP model,...
Social networks such as Twitter and Facebook have become important sources for users to acquire information. In those social networks, users obtain information from the posts/reposts of their social connections. To acquire information efficiently, users are motivated to connect to users who offer attractive and timely information. In this paper, we study how to effectively optimize social connections...
Computationally expensive problems pose a serious challenge to the successful application of evolutionary algorithms to complex engineering optimization. To address this challenge, surrogate models, also known as metamodels, are commonly used in lieu of the expensive fitness function for the computational cost of optimization. However, it is nontrivial to choose an appropriate metamodel for properly...
Functional connectivity, which is indicated by time-course correlations of brain activities among different brain regions, is one of the most useful metrics to represent human brain states. In functional connectivity analysis (FCA), the whole brain is parcellated into a certain number of regions based on anatomical atlases, and the mean time series of brain activities are calculated. Then, the correlation...
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