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Dimensionality reduction techniques play an essential role in data analytics, signal processing, and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes...
Methods to cluster fingerprints based on Smallest-Enclosing-Circle (SEC) and to modify Weighted-K-Nearest-Neighbor (WKNN) matching algorithm for indoor fingerprint positioning system are proposed. Based on the approach to computing the smallest k-enclosing circle, the method proposed clusters fingerprints in database by introducing reference points' coordinates, instead of their received signal strength...
Many real-life datasets exhibit structure in the form of physically meaningful clusters - e.g., news documents can be categorized as sports, politics, entertainment, and so on. Taking these clusters into account together with low-rank structure may yield parsimonious matrix and tensor factorization models and more powerful data analytics. Prior works made use of data-domain similarity to improve nonnegative...
Nowadays in order to process and store many kinds of multimedia data, the storage capability of memory has grown greatly. Moreover the widespread use of mobile devices and cloud computing has made criminal investigators often face a lot of memory dumps. They have to deal with a large quantity of memory data and complex OS data structures which they have little knowledge of. How to analyze memory evidence...
We consider the scenario of an unknown overdetermined instantaneous mixture of quasi-stationary sources. Blind source separation (BSS) under this scenario has drawn much attention, motivated by applications such as speech and audio separation. The ideas in the existing BSS works often focus on exploiting the time-varying statistics characteristics of quasi-stationary sources, through various kinds...
A reliable and efficient clustering algorithm in wireless sensor networks is proposed, which can allow the structured representation of a network topology. According to the residual energy, number of the neighbors, and centrality of each node, the algorithm uses fuzzy petri nets to select cluster head. The algorithm not only balances the energy load of each node, but also provides global reliability...
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