Low-Rank Kernel-Based Semisupervised Discriminant Analysis

Table 5

Classification accuracy of different graphs with varying noise on Musk database.

Noise types	Algorithm	Variance or density of the three noises
		0	0.02	0.04	0.06	0.08	0.1
Gaussian	LRKSDA	0.838095	0.783333	0.810476	0.795238	0.789524	0.777619
Gaussian	KSDA1	0.756849	0.689112	0.705138	0.702206	0.699312	0.710083
Gaussian	KSDA2	0.755128	0.705054	0.695936	0.697523	0.695125	0.70306
Gaussian	SDA	0.757407	0.713289	0.699202	0.714286	0.676558	0.681785
“Salt and pepper”	LRKSDA	0.838095	0.785238	0.771429	0.772143	0.766429	0.761905
“Salt and pepper”	KSDA1	0.756849	0.683009	0.667079	0.656237	0.66388	0.653854
“Salt and pepper”	KSDA2	0.755128	0.705003	0.664427	0.658723	0.656934	0.652174
“Salt and pepper”	SDA	0.757407	0.70503	0.697131	0.681818	0.678207	0.666734
Multiplicative	LRKSDA	0.838095	0.832381	0.827143	0.809524	0.793333	0.784286
Multiplicative	KSDA1	0.756849	0.733777	0.723228	0.71144	0.716774	0.71115
Multiplicative	KSDA2	0.755128	0.737889	0.716812	0.710216	0.701506	0.68323
Multiplicative	SDA	0.757407	0.749432	0.738486	0.726044	0.703764	0.68799