A Review on Fixed-Rank Representation for Supervised Learning Using Neural Network
نویسندگان
چکیده
This study focused on development and application of efficient algorithm for clustering and classification of supervised visual data. In machine learning clustering classification and clustering is most useful techniques in pattern recognition and computer vision. Existing techniques for subspace clustering makes use of rank minimization and spars based that are computationally expensive and may result in reducing the clustering performance. The algorithm called fixed rank representation based on matrix factorization is used to partially solve the problem of existing system. FRR perform classification using neural network algorithm. Neural network takes patterns as input from the FRR by performing rank minimization techniques in LRR and then performs classification. FRR can be able to solve the problem of multiple subspace clustering. Using neural network FRR can be able to give good classification of given pattern. Keywords— Supervised visual data classification, SSC, LRR, FFR, Neural Network
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