12. A system for automatically diagnosing new faults in a device, said system comprising: a processor; a data bus coupled to said processor; and a non-transitory computer-usable medium embodying computer program code, said non-transitory computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for: mapping known faults to user-defined classes to derive a plurality of known classes; configuring a classifier based on training data, wherein said classifier assigns image quality based feature vectors to at least one known class among said plurality of known classes; generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier; clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect and a drift in the probabilistic distribution of the plurality of members between the core class and the at least one outlier cluster in a known defect in the device overtime, wherein the device is a rendering device and the known defect is an image quality defect; and determining if a probabilistic distribution of said plurality members of said outlier cluster associated with said class quantifies a change in distribution when a set of spurious features becomes sufficiently different from said corresponding distribution of said core class members to therefore indicate said new faults and said drift in said known defect and thereafter diagnose said new faults and said drift in said known defect in said device, wherein said new faults are unknown image quality defects; and retraining said classifier with updated characteristics of said new defect.
12. A system for automatically diagnosing new faults in a device, said system comprising: a processor; a data bus coupled to said processor; and a non-transitory computer-usable medium embodying computer program code, said non-transitory computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for: mapping known faults to user-defined classes to derive a plurality of known classes; configuring a classifier based on training data, wherein said classifier assigns image quality based feature vectors to at least one known class among said plurality of known classes; generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier; clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect and a drift in the probabilistic distribution of the plurality of members between the core class and the at least one outlier cluster in a known defect in the device overtime, wherein the device is a rendering device and the known defect is an image quality defect; and determining if a probabilistic distribution of said plurality members of said outlier cluster associated with said class quantifies a change in distribution when a set of spurious features becomes sufficiently different from said corresponding distribution of said core class members to therefore indicate said new faults and said drift in said known defect and thereafter diagnose said new faults and said drift in said known defect in said device, wherein said new faults are unknown image quality defects; and retraining said classifier with updated characteristics of said new defect. 20. The system of claim 12 wherein said instructions are further configured for: assigning a label to said new defect based on a plurality of known faults utilizing said classifier; and associating a support vector machine classifier with said classifier.