Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical valueto-value cluster coupling learning. Unlike existing embedding-and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster values with different granularities. It further models the couplings in value clusters within the same granularity and with different granularities to embed feature values into a new numerical space with independent dimensions. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods.
Aircraft wake is a pair of strong counter-rotating vortices generated behind a flying aircraft. It might be very hazardous to a following aircraft and the real-time detection of it is of great interest in aviation safety field. Vortex-core positions and velocity circulations, which respectively represent the location and strength of a wake, are two characteristic parameters that have attracted the main attention in wake vortex detection. This paper introduces a new algorithm, the Path Integration (PI) method, to retrieve the characteristic parameters of wake vortex. The method uses Doppler velocity distribution to locate the vortex-core positions, and the integration of Doppler velocity along a LOS (line-of-sight) is derived as a linear expression about the circulations. From this expression, the circulations can be solved with the least square method. Moreover, an vortex-core position adjusting method is proposed to compensate the compressing and expanding effects of wake vortex caused by the scanning of Lidar beam. Basically, the use of Doppler velocity integration can improve the method’s adaptability in turbulence environment and mitigate the impact of noise. Numerical examples and field detection data from Hong Kong international airport and Tsingtao Liuting airport have well verified the good performance of the method, in terms of both accuracy and efficiency.
Real data often consists of multiple views (or representations). By exploiting complementary and consensus grouping information of multiple views, multi-view clustering becomes a successful practice for boosting clustering accuracy in the past decades. Recently, researchers have begun paying attention to the problem of incomplete view. Generally, they assume at least there is one complete view or only focus on two view problems. However, above assumption is often broken in real tasks. In this work, we propose an IVC algorithm for clustering with more than two incomplete views. Compared with existing works, our proposed algorithm (1) does not require any view to be complete, (2) does not limit the number of incomplete views, and (3) can handle similarity data as well as feature data. The proposed algorithm is based on the spectral graph theory and the kernel alignment principle. By aligning projections of individual views with the projection integration of all views, IVC exchanges the complementary grouping information of incomplete views. Consequently, projections of individual views are made complete and thereby resulting the consensus with accurate grouping information. Experiments on synthetic and real datasets demonstrate the effectiveness of IVC.
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