Browsing by Subject "clustering"

Now showing 1 - 4 of 4
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    Big-video mining of road appearances in full spectrums of weather and illuminations
    (IEEE, 2017-10) Cheng, Guo; Wang, Zheyuan; Zheng, Jiang Yu; Computer and Information Science, School of Science
    Autonomous and safety driving require the control of vehicles within roads. Compared to lane mark tracking, road edge detection is more difficult because of the large variation in road and off-road materials and the influence from weather and illuminations. This work investigates visual appearances of roads under a spectrum of weather conditions. We use big-data mining on large scale naturalistic driving videos taken over a year through four seasons. Large video volumes are condensed to compact road profile images for analysis. Clusters are extracted from all samples with unsupervised learning. Typical views of a spectrum of weather/illuminations are generated from the clusters. Further, by changing the number of clusters we find a stable number for clustering. The learned data are used to classify driving videos into typical illumination types briefly. The surveyed data can also be used in the development of road edge detection algorithm and system as well as their testing.
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    Kernelized Sparse Self-Representation for Clustering and Recommendation
    (SIAM, 2016) Bian, Xiao; Li, Feng; Ning, Xia; Department of Computer and Information Science, School of Science
    Sparse models have demonstrated substantial success in applications for data analysis such as clustering, classification and denoising. However, most of the current work is built upon the assumption that data is distributed in a union of subspaces, whereas limited work has been conducted on nonlinear datasets where data reside in a union of manifolds rather than a union of subspaces. To understand data nonlinearity using sparse models, in this paper, we propose to exploit the self-representation property of nonlinear data in an implicit feature space using kernel methods. We propose a kernelized sparse self-representation model, denoted as KSSR, and a novel Kernelized Fast Iterative Soft-Thresholding Algorithm, denoted as K-FISTA, to recover the underlying nonlinear structure among the data. We evaluate our method for clustering problems on both synthetic and real-world datasets, and demonstrate its superior performance compared to the other state-of-the-art methods. We also apply our method for collaborative filtering in recommender systems, and demonstrate its great potential for novel applications beyond clustering.
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    Name Disambiguation in Anonymized Graphs using Network Embedding
    (ACM, 2017) Zhang, Baichuan; Al Hasan, Mohammad; Computer and Information Science, School of Science
    In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.
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    Semantic Search in Psychosis: Modeling Local Exploitation and Global Exploration
    (Oxford, 2020-04-20) Lundin, Nancy B.; Todd, Peter M.; Peter M., Michael N.; Avery, Johnathan E.; O’Donnell, Brian F.; Hetrick, William P.; Psychiatry, School of Medicine
    Impairments in category verbal fluency task (VFT) performance have been widely documented in psychosis. These deficits may be due to disturbed “cognitive foraging” in semantic space, in terms of altered salience of cues that influence individuals to search locally within a subcategory of semantically related responses (“clustering”) or globally between subcategories (“switching”). To test this, we conducted a study in which individuals with schizophrenia (n = 21), schizotypal personality traits (n = 25), and healthy controls (n = 40) performed VFT with “animals” as the category. Distributional semantic model Word2Vec computed cosine-based similarities between words according to their statistical usage in a large text corpus. We then applied a validated foraging-based search model to these similarity values to obtain salience indices of frequency-based global search cues and similarity-based local cues. Analyses examined whether diagnosis predicted VFT performance, search strategies, cue salience, and the time taken to switch between vs search within clusters. Compared to control and schizotypal groups, individuals with schizophrenia produced fewer words, switched less, and exhibited higher global cue salience, indicating a selection of more common words when switching to new clusters. Global cue salience negatively associated with vocabulary ability in controls and processing speed in schizophrenia. Lastly, individuals with schizophrenia took a similar amount of time to switch to new clusters compared to control and schizotypal groups but took longer to transition between words within clusters. Findings of altered local exploitation and global exploration through semantic memory provide preliminary evidence of aberrant cognitive foraging in schizophrenia.