Abstract:
Data mining and machine learning use matrix factorization (MF), an unsupervised data representation technique. Different application scenarios can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data and learns the compact representation corresponding to the basis.
Most previous data mining work on MF has ignored finding such a basis, which can carry high-order semantics in the data. Joint Hypergraph Embedding and Sparse Coding (JHESC) is a new MF framework that captures high-order semantic information in data.
We propose a new hypergraph learning model to obtain a more discriminative basis by hypergraph-based Laplacian Eigenmap, then sparse code the learned basis to improve identification capability. To better handle nonlinear data, we extend the method to the reproducing kernel Hilbert space.
Extensive data clustering experiments show that the proposed matrix factorization method outperforms other state-of-the-art methods.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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