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With its high-dimensional state and action space, large-scale multi-agent reinforcement learning (MARL) is a challenging problem. Centralized approximate RL is impractical to deal with this because the search cost grows exponentially with the number of agents. Further, traditional decentralized approaches require delicate model-specific decomposition and communication within multi-agent system (MAS)...
Visual object tracking is a fundamental task in many high-level computer vision applications. Most existing algorithms have to build complex models with expensive computation to achieve accurate object tracking, which brings significant difficulty in real-time tracking. In order to address this problem, motivated by recent success of high-speed correlation filter (CF) models, a novel real-time object...
Illumination condition is one of the most important factors that affect the face recognition performance. Face image illumination quality assessment can predict the face recognition performance under various illumination conditions, which will improve the accuracy and efficiency of the face recognition system. However, the quality scores calculated by the existing methods are weakly correlated with...
Crash hotspot detection is important to reduce traffic crashes by allowing effective deployment of countermeasures in those locations. However, current hotspot detection methods rely mostly on crash occurrences and, therefore, countermeasures can be implemented only after a number of crashes have been occurred. To prevent crashes prior to their actual occurrences, crash precedents, also known as surrogate...
Recently, deep learning became very popular, and was applied to many fields. The convolutional neural networks are often used for representing the layers for deep learning. In this paper, we propose Convolutional Self Organizing Map, which can be applicable to deep learning. Conventional Self Organizing Map uses single layered architecture, and can visualizes and classifies the input data on 2 dimensional...
It has been known that SNPs are the most important types of genetic variations that can influence common diseases and phenotypes. Increasing number of SNP-phenotype related publications, demonstrate the need for an automatic extraction of this association from biomedical articles. Although few corpora have been developed for obtaining the mutation and disease from text, no corpus is available which...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
GAT (Global Affine Transformation) and GPT (Global Projection Transformation) correlation matchings were successively proposed by Wakahara and Yamashita which use affine transformation (AT) and 2D projection transformation (PT), respectively, to maximize the normalized cross-correlation value between a template and a GAT/GPT-superimposed input image. In theory, to maximize the degree of matching via...
We consider a reduced dimensionality representation based on multiple views of the same underlying process. These multiple views can be obtained, for example, using several different modalities, measured with different instrumentation or generated based on different methods of feature extractions. Our framework is based on a cross-view random walk process which is restrained to hop between the different...
This paper presents a new approach to the production of feature maps for the improvement of classification in machine learning. The idea is based on a calculus of differentiation and integration of feature vectors, which can be viewed as functions on a metric space or network. Based on this we propose a novel network-based binary machine learning classifier. We illustrate our method using molecular...
Understanding ongoing topics and their evolutions in social media is of great importance. Although topic analysis is not a novel research question, social media environment has presented new challenges. First, with insufficient co-occurrence information, short text have undermined many word co-occurrence oriented topic models' applicability. Second, real time message streams make traditional discretized...
Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps...
Traditionally, investors try to estimate short term portfolio volatility based on daily return. When tick-by-tick data are available, investors use different volatility estimators based on high-frequency data to evaluate the portfolio risk in the hope of outperforming those based on low-frequency data. In this paper, we optimize block realized kernel estimator in Hautsch et al. (2015) and propose...
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizophrenia and 104 healthy controls. Kernel and Multiple...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
With the increasing availability of multi-view nonnegative data in practical applications, multi-view learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, previous works are either difficult to generate meaningful clustering results in terms of views with heterogeneous quality, or sensitive to noise. To address these problems, we propose a co-regularized...
Occlusion is a challenging problem in visual object tracking. Most state-of-the-art trackers may learn the appearance of the occluding target when it becomes occluded by other objects in the scene. This paper proposes a novel approach of detecting occlusion by dividing the target into several patches and computing the peak-to-sidelobe ratio of every response map. Furthermore, our method can calculate...
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based...
Kernel independent component analysis (KICA) detects primary independent components of data by minimizing kernelized canonical correlation of random variables in a reproducing kernel Hilbert space. KICA has been widely used in many practical tasks, e.g., blind source separation and speech recognition. However, the dense kernel matrix in traditional KICA causes high computational complexity which prohibits...
Person re-identification aims to match people across non-overlapping camera views. One of the challenges in re-identification is cross view matching, where the gallery and query data belong to different views. This problem is difficult because the person's appearance varies greatly due to significant viewpoint and poses changes. In this paper, we perform Kernel Canonical Correlation Analysis (KCCA)...
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