Sparse Kernel Machines for Discontinuous Registration and Nonstationary Regularization

2016 ◽  
Author(s):  
Christoph Jud ◽  
Nadia Mori ◽  
Philippe C. Cattin
Keyword(s):  
Kernel Machines ◽  
Sparse Kernel ◽  
Nonstationary Regularization

Constructing Sparse Kernel Machines Using Attractors

2009 ◽  
Vol 20 (4) ◽  
pp. 721-729 ◽  
Author(s):  
Daewon Lee ◽  
Kyu-Hwan Jung ◽  
Jaewook Lee
Keyword(s):  
Kernel Machines ◽  
Sparse Kernel

scholarly journals Music Emotion Detection Using Hierarchical Sparse Kernel Machines

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yu-Hao Chin ◽  
Chang-Hong Lin ◽  
Ernestasia Siahaan ◽  
Jia-Ching Wang
Keyword(s):  
Probability Distributions ◽  
Relevance Vector Machine ◽  
Radial Basis Function Kernel ◽  
Kernel Machines ◽  
Emotion Detection ◽  
Product Kernel ◽  
Decision Vector ◽  
Music Clip ◽  
Sparse Kernel ◽  
Target Side

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

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