Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment
نویسندگان
چکیده
منابع مشابه
Multiple Kernel Clustering with Local Kernel Alignment Maximization
Kernel alignment has recently been employed for multiple kernel clustering (MKC). However, we find that most of existing works implement this alignment in a global manner, which: i) indiscriminately forces all sample pairs to be equally aligned with the same ideal similarity; and ii) is inconsistent with a well-established concept that the similarity evaluated for two farther samples in a high ...
متن کاملLocal Self-concordance of Barrier Functions Based on Kernel-functions
Many efficient interior-point methods (IPMs) are based on the use of a self-concordant barrier function for the domain of the problem that has to be solved. Recently, a wide class of new barrier functions has been introduced in which the functions are not self-concordant, but despite this fact give rise to efficient IPMs. Here, we introduce the notion of locally self-concordant barrier functio...
متن کاملRatio-Based Multiple Kernel Clustering
Maximum margin clustering (MMC) approaches extend the large margin principle of SVM to unsupervised learning with considerable success. In this work, we utilize the ratio between the margin and the intra-cluster variance, to explicitly consider both the separation and the compactness of the clusters in the objective. Moreover, we employ multiple kernel learning (MKL) to jointly learn the kernel...
متن کاملObject Recognition based on Local Steering Kernel and SVM
The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...
متن کاملMultiple Kernel Clustering
Maximum margin clustering (MMC) has recently attracted considerable interests in both the data mining and machine learning communities. It first projects data samples to a kernel-induced feature space and then performs clustering by finding the maximum margin hyperplane over all possible cluster labelings. As in other kernel methods, choosing a suitable kernel function is imperative to the succ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers, Materials & Continua
سال: 2019
ISSN: 1546-2226
DOI: 10.32604/cmc.2019.06206