鎶ュ憡棰樼洰錛?Atomic Representation-based Classification: Theory, Algorithm and Applications
銆€銆€鎶ュ憡浜猴細鏉庤惤娓?錛堟箹鍖楀ぇ瀛?鏁欐巿錛?br /> 銆€銆€鎶ュ憡鏃墮棿錛?018騫?1鏈?鏃?nbsp; 15:30
銆€銆€鎶ュ憡鍦扮偣錛氭牸鑷翠腑妤?00
銆€銆€鎶ュ憡鎽樿錛?Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. In this talk, we introduce a new condition called atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We establish the theoretical guarantees for a general unified framework termed as atomic representation-based classification (ARC), which includes most RC methods as special cases. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data.
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