Name
Abstract | ||
|---|---|---|
In this paper, we develop a new effective multiple kernel learning algorithm. First, map the input data into m different feature spaces by m empirical kernels, where each generatedfeature space is takenas one viewof the input space. Then through the borrowing the motivating argument from Canonical Correlation Analysis (CCA)that can maximally correlate the m views in the transformed coordinates, we introduce a special term called Inter-Function Similarity Loss R IFSL into the existing regularization framework so as to guarantee the agreement of multi-view outputs. In implementation, we select the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the incorporated paradigm, and the experimental results on benchmark data sets demonstrate the feasibility and effectiveness of the proposed algorithm named MultiK-MHKS. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TPAMI.2007.70786 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
different kernel,benchmark datasets,m different feature space,squared approximation,ho-kashyap algorithm,index terms— multiple kernel learning,alternative formulation,inter-function similarity loss,single learning process,modified ho-kashyap algo- rithm,proposed algorithm,novel multiple kernel learning,canonical correlation analysis,regularization learning,pattern recognition.,optimization problem,kernel,approximation algorithms,genomics,sun,indexing terms,learning artificial intelligence,pattern recognition,pattern analysis,support vector machines,feature space,algorithm design and analysis | Kernel (linear algebra),Approximation algorithm,Similitude,Feature vector,Algorithm design,Pattern recognition,Computer science,Support vector machine,Multiple kernel learning,Algorithm,Artificial intelligence,Kernel method | Journal |
Volume | Issue | ISSN |
30 | 2 | 0162-8828 |
Citations | PageRank | References |
59 | 1.91 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Wang | 1 | 268 | 18.89 |
| Songcan Chen | 2 | 4148 | 191.89 |
| Tingkai Sun | 3 | 303 | 10.58 |