4. A computer-implemented method for automatically diagnosing new faults in a device, said method comprising: said computer mapping known faults to user-defined classes to derive a plurality of known classes; said computer 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; said computer generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier; said computer 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 said computer 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 said computer retraining said classifier with updated characteristics of said new defect.
4. A computer-implemented method for automatically diagnosing new faults in a device, said method comprising: said computer mapping known faults to user-defined classes to derive a plurality of known classes; said computer 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; said computer generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier; said computer 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 said computer 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 said computer retraining said classifier with updated characteristics of said new defect. 9. The method of claim 4 further comprising: said computer extracting a raw image from a test pattern; and said computer transmitting said raw image to said classifier in the event said new defect is identified with respect to an image quality associated with said device.