×
In this paper, a novel partitioning clustering method called KCK-means is proposed based on KCCA. We also show that KCK-means can not only be run on two-view ...
In this paper, we propose two algorithms based on KCCA which can improve the performances of traditional clustering algorithms—K-means, namely KCK-means for two ...
This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA).
Missing: KCK- | Show results with:KCK-
In this paper, a novel partitioning clustering method called KCK-means is proposed based on KCCA. We also show that KCK-means can not only be run on two-view ...
People also ask
This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA).
Missing: KCK- | Show results with:KCK-
For the K-Means clustering algorithm, it is often used in emotion recognition, and it has achieve- ments in the feature extraction of speech [Sultana and.
Missing: KCK- | Show results with:KCK-
Sep 25, 2014 · A clustering method based on kernel Canonical correlation analysis. Dr. Yingjie Tian. Outline. Motivation & Challenges
In this paper we present cluster canonical cor- relation analysis (cluster-CCA) for joint dimen- sionality reduction of two sets of data points.
Missing: KCK- | Show results with:KCK-
Kernel Canonical Correlation Analysis (KCCA) is a technique that can extract common features from a pair of multivariate data, which may assist in mining ...
Kck-means: A clus- tering method based on kernel canonical correlation analy- sis. In Computational Science–ICCS 2008, pages 995–1004. Springer, 2008. [8] ...