Then, it constructs indexing tree by conducting GMM clustering on high-dimensional multimedia data layer by layer. RESULTS: The presented GMM-based high-dimensional multimedia data indexing method was verified under small data and big data environment. The retrieval accuracy of GMM clustering tree is...
;k,δk三个参数,或者换一种思路理解,第k个高斯分布生成的数据为r(i,k)xi(i=1~N),然后利用最大似然估计即可求出μk,δk以及πk,求解公式为:3)判断GMM的似然函数的值是否收敛,若收敛,则停止,否则重复迭代1),2)步。 详见: 漫谈 Clustering (3):GaussianMixtureModel高斯混合模型(GMM)及其EM ...
Two basic algorithms may be used in this clustering process: (i) k-means clustering—dividing the objects into k clusters so that some metric relative to the centroids of the clusters is minimized, (ii) spectral clustering—finding data points as nodes of a connected graph and partitioning ...
[31] proposed the joint registration multiple PC (JRMPC) method, considering multiple point clouds as realizations of a latent GMM and transforming PCR into a clustering problem. Compared with GMM-based methods, HMM-based methods [9,10,11,12] used von Mises–Fisher (VMF) to model the ...
To conduct data clustering on the streaming data, this paper proposes a novel incremental clustering approach utilizing Gaussian Mixture Model (GMM), termed as ICGT (Incremental Construction of GMM Tree). The ICGT creates and dynamically adjusts a GMM tree consistent to the sequentially presented ...
Some current studies use the EM algorithm to iteratively update the parameters of the model continuously when clustering is performed by Gaussian mixture models, and finally find the optimal parameters. However, the research has shown that [21] the EM algorithm has convergence and can be influenced...
The calculation procedure of the FCM clustering algorithm involves seven steps: Step1: Determining the class number (Q) of samples; Step2: Determining the membership factor (m), number of iterations (T), and convergence condition (ɛɛ); Step3: Initializing the membership matrix (U) and the...
ACID/HNN: clustering hierarchies of neural networks for context-dependent connectionist acoustic modeling We present the ACID/HNN framework, a principled approach to hierarchical connectionist acoustic modeling in large vocabulary conversational speech recognit... Ju¨rgen Fritsch,M Finke - IEEE 被引量: ...
Abstract In this study, we propose algorithms based on subspace learning in the \{GMM\} mean supervector space to improve performance of speaker clustering with speech from both reading and singing. As a speaking style, singing introduces changes in the time-frequency structure of a speaker’s ...
First, it is proposed to replace gender and age byunsupervised clustering. Speaker classes are first used for adaptation of the conventional HMM. Second, speaker classes are used for initializing structured GMM, where the components of Gaussian densities are structured with respect to the speaker ...