所谓多视角,引用原文:1. Different to single-view clustering using singular data descriptor, in this paper, we first describe each data point (e.g., an image) by various features (e.g., different image descriptors, such as HOG, Color Histogram and GIST) and then feed these features from m...
Clustering on such multi-view data is called incomplete multi-view clustering (IMC). Most of the existing IMC solutions are offline and have high computational and memory costs especially for large-scale datasets. To tackle these challenges, in this paper, we propose a Online Binary Incomplete ...
Fig. 15 View in articleFull size image Fig. 16 View in articleFull size image Fig. 17 View in articleFull size image Fig. 18 View in articleFull size image Agarwal, S., Lim, J., Zelnik-Manor, L., Perona, P., Kriegman, D., Belongie S.: Beyond pairwise clustering. In: CVPR (...
Herschel, Boole, and others extended this study to the demonstration that clustering is a general phenomenon of gravitational systems. The discovery of the wobble in the proper motion of Sirius led Bessel, in the 1840 s, to argue for the presence of a low-mass, then unseen companion; it ...
The graph kernels are used in kernel-based data mining and machine learning algorithms, most commonly support vector machines (SVMs), but can also be exploited for tasks such as clustering. In the past, many graph kernels have been proposed that are tailored towards specific application [113–...
Categorize support issues (multiclass classification) Predict prices (regression) Categorize iris flowers (k-means clustering) Recommend movies (matrix factorization) Image classification (transfer learning) Classify images (model composition) Forecast bike rental demand (time series) Call-volume spikes (ano...
(QK-Means) clustering technique to discriminate quantum states on the IBM Bogota quantum device19. Apart from universal quantum computing approaches, adiabatic quantum machine learning approaches have also been proposed for traditional machine learning models such as regression and k-means clustering20,21...
Categorize support issues (multiclass classification) Predict prices (regression) Categorize iris flowers (k-means clustering) Recommend movies (matrix factorization) Image classification (transfer learning) Classify images (model composition) Forecast bike rental demand (time series) ...
Asghari, S., Nematzadeh, H., Akbari, E., Motameni, H.: Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering. Multimedia Tools Appl. 82, 42617–42639 (2023) Article Google Scholar Zhang, Y., et al.: Multivariate approach for Alzheimer...
However, there are some algorithms that deal with categorical features clustering such as the k-dimensional clustering algorithm (Al-Jabery et al., 2016). This algorithm can handle categorical features without the need to recode or normalize them. View chapter Book 2020, Computational Learning ...