Low-Rank Kernel-Based Semisupervised Discriminant Analysis

Table 4

Classification accuracy of different graphs with varying noise on Yale B database.

Noise types	Algorithm	Variance or density of the three noises
		0	0.02	0.04	0.06	0.08	0.1
Gaussian	LRKSDA	0.825769	0.816429	0.814286	0.807857	0.808214	0.807143
Gaussian	KSDA1	0.691392	0.555408	0.565422	0.562556	0.574249	0.579816
Gaussian	KSDA2	0.723549	0.585366	0.597015	0.602456	0.590576	0.59866
Gaussian	SDA	0.668397	0.543879	0.540266	0.542199	0.541947	0.543264
“Salt and pepper”	LRKSDA	0.825769	0.794643	0.7675	0.711786	0.643929	0.599286
“Salt and pepper”	KSDA1	0.691392	0.56888	0.509246	0.474557	0.450803	0.436003
“Salt and pepper”	KSDA2	0.723549	0.59498	0.522505	0.478096	0.446308	0.43581
“Salt and pepper”	SDA	0.668397	0.553305	0.498777	0.468533	0.452681	0.429647
Multiplicative	LRKSDA	0.825769	0.825357	0.821429	0.82	0.814286	0.793929
Multiplicative	KSDA1	0.691392	0.631297	0.619849	0.594597	0.584588	0.576168
Multiplicative	KSDA2	0.723549	0.641188	0.622062	0.616446	0.594529	0.594516
Multiplicative	SDA	0.668397	0.594035	0.588897	0.58513	0.582225	0.556328