Patent Publication Number: US-7917540-B2

Title: Nonlinear set to set pattern recognition

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims benefit of U.S. Provisional Application No. 60/903,102, entitled “Multivariate State Estimation Technique for Pattern Recognition and Multi-class Image Classification” and filed on Feb. 22, 2007, which is specifically incorporated by reference herein for all that it discloses and teaches. 
     The present application is also related to co-pending U.S. patent application Ser. No. 11/846,486, entitled “Set to Set Pattern Recognition” and filed on Aug. 28, 2007. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This technology was developed with sponsorship by the National Science Foundation Contract No. DMS-0434351, and the Air Force Office of Scientific Research Contract No. FA9550-04-1-0094 P00002, and the government has certain rights to this technology. 
    
    
     BACKGROUND 
     Face recognition technology is a type of pattern recognition used to identify an individual based on video or still frame images of the individual&#39;s face. Typically, a data set of images of the individual&#39;s face (i.e., a specific type of pattern) is first collected and then a face image of an unknown individual is evaluated relative to this data set. Traditional face recognition has focused on individual comparisons between single images. As such, if the unknown face image sufficiently matches one or more of the data sets of the known individual, the unknown face image may be classified as that of the individual. 
     Typically, however, the initial data set of images tends to include substantial variations of state (e.g., in illumination and pose) that make the evaluation with the unknown face image difficult to resolve. In one existing approach, illumination and/or pose variations, for example, in the data set of images are removed by computing illumination and/or invariant images to obtain a more normalized data set. Likewise, an illumination and/or pose invariant versions of the unknown image may also be computed. Unfortunately, such normalization discards or obfuscates unique characteristics of each image. 
     SUMMARY 
     Implementations described and claimed herein address the foregoing problems, among others, by recognizing that variations in the states of patterns can be exploited for their discriminatory information and should not be discarded as noise. A pattern recognition system compares a data set of unlabeled patterns having variations of state in a set-by-set comparison with labeled arrays of individual data sets of multiple patterns also having variations of state. The individual data sets are each mapped to a point on a parameter space, and the points of each labeled array define a subset of the parameter space. If the point associated with the data set of unlabeled patterns satisfies a similarity criterion on the parameter space subset of a labeled array, the data set of unlabeled patterns is assigned to the class attributed to that labeled array. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. It should also be understood that, although specific implementations are described herein, the described technology may be applied to other systems. 
    
    
     
       BRIEF DESCRIPTIONS OF THE DRAWINGS 
         FIG. 1  illustrates example images allocated into data sets of labeled arrays. 
         FIG. 2  illustrates an example system for performing set-to-set pattern recognition. 
         FIG. 3  illustrates an example relationship defined between a labeled array of data sets and an associated subset of a parameter space. 
         FIG. 4  illustrates an example relationship defined between another labeled array of data sets and an associated subset of a parameter space, relative to a different subset of the parameter space associated with a different array of data sets. 
         FIG. 5  illustrates example operations for performing set-to-set pattern recognition. 
         FIG. 6  illustrates components of an example system that can be useful in the implementation of the described technology. 
     
    
    
     DETAILED DESCRIPTIONS 
     The described technology takes advantage of variations in multiple states of a pattern drawn from a family of patterns. These variations can be exploited to improve association of unidentified sets of patterns to labeled sets of patterns (e.g., classification). As such, collected data with variations of state (e.g., variations in illumination and pose of an image subject) can be employed to improve pattern recognition accuracy. 
     Generally, set-to-set pattern recognition is performed in at least one implementation by encoding (e.g., mapping) a data set of patterns abstractly as a point on a parameter space. The term “point” is general and results from the mapping of a data set (e.g., images or other patterns) as a point on a Grassmann manifold, a Stiefel manifold, a flag manifold, or other parameter space. In one implementation, a data set of patterns is processed to define a subspace represented as a point on a Grassmannian. In another implementation, the data set of patterns is processed to define an ordered set of orthonormal vectors as a point on a Stiefel manifold. Other mappings are also contemplated, such as to a point on a flag manifold or a product of manifolds. By these example applications, each data set of patterns is mapped to a point on the parameter space. 
     It is helpful to describe this technology in relation to a specific application, such as image recognition, although it should be understood that many other applications may benefit from this technology. Other applications may include detecting patterns of control and sensor signals in a manufacturing facility, a power plant, a communications system, a vehicle, etc. and monitoring patterns in radar signals or remote sensor signals. 
       FIG. 1  illustrates example images allocated into data sets of labeled arrays  100  and  102 . The labeled arrays  100  and  102  represent training data that can be used by a classifier to classify a data set of unlabeled images. Each labeled array  100  and  102  represents data designated as a member of a particular class of a set of N classes (e.g., the array  100  is labeled as Class  1  and the array  102  is labeled as Class N). The dots between the labeled arrays  100  and  102  indicate that additional labeled arrays may also exist in the training data. To facilitate the description,  FIG. 1  shows that the individual arrays may be represented by the labeled array icons  106  and  108 . 
     Each column in a labeled array represents a data set of images (e.g., data set  104 ) with variations in state (e.g., illumination) within each data set. The dots between the data sets of a labeled array indicate that additional data sets may also exist in the labeled array. An individual image is termed an element of a data set, and the dots within each data set indicated that additional elements may also exist within a data set. In the illustrated example, the pose of the subject (e.g., the subject is facing the camera head-on in the left-most column of Class  1  and is nearly showing a right profile in the right-most column of Class  1 ) varies among data sets. 
     Using the image recognition context as an example, each data set of patterns (e.g., images) is allocated into a labeled array of data sets (e.g., images of a single person), such that there are multiple (1 to N) labeled arrays of data sets, each labeled array being designated to a class (class  1  to class N). In this manner, data sets of each class are collected into their own labeled array. For example, a collection of images of a first individual are allocated to an array labeled with the individual&#39;s name “John Smith” (i.e., a first class). Within each array, the images of John Smith are allocated into different data sets, each data set pertaining to a different pose (e.g., head-on, right profile, left profile, etc.). Within each data set, the images of John Smith may have variations in illumination from one to another but share the same pose. 
     It should also be understood that variations of state need not be partitioned relative to individual data sets in any particular fashion, although in the example of  FIG. 1 , different data sets have been described as containing images of distinct poses. Further, it should be understood that the data in the individual elements of each data set may represent any kind of pattern—images are merely example data set elements. 
       FIG. 2  illustrates an example system  200  for performing set-to-set pattern recognition. A pre-processor  202  receives a collection  204  of labeled arrays of multiple labeled images. Each array, and therefore each data set and each image, is labeled with a class designation  1  through N (e.g., a class designation may indicate the subject&#39;s identity). Each data set within a labeled array represents multiple observations of a member of the class, possibly in multiple modalities (e.g., audio and video data). Accordingly,  FIG. 2  shows a collection  204  of data set arrays, where each labeled array is represented as X i  for i=1, . . . N. Each array is labeled as originates from one of N-classes (e.g., individual identities) or families of patterns, represented as ω 1 , ω 2 , ω 3 , . . . ω N . 
     Each observation in a data set and each labeled array has a shared characteristic, such as being associated with the same individual, the same object, etc. In one implementation, for example, each data set includes multiple images having a variation of state distinguishing each image in the data set from each other image in the data set. For example, in the data set  104  of  FIG. 1 , each image depicts the same individual under variations of state (e.g., illumination variations) and a first pose (e.g., head-on view). Likewise, the data set  110  includes images of the same individual under different variations of state (e.g., illumination variations) and a second pose (e.g., head turned to subject&#39;s left). In contrast, in the data set  112  of  FIG. 1 , each image depicts a different individual than the man depicted in data sets  104  and  110 . Nevertheless, the images in data set  112  depict the same individual within data set  112  under variations of state (e.g., illumination variations) and a first pose (e.g., head-on view). Likewise, the data set  114  includes images of the same individual as data set  112  under different variations of state (e.g., illumination variations) and a second pose (e.g., head turned to subject&#39;s right). Variations of multiple states simultaneously, such as pose and illumination varying together, provides significant additional information that can be employed in a fashion similar to the variation of a single state for enhanced classification. 
     In an alternative implementation, the shared characteristic is not limited to an “identity” of the subject but instead can be a shared characteristic among multiple distinct objects. For example, the observations may be images of different tissue samples having the same pathology (e.g., all of the tissue samples in a data set have a particular type of cancerous cells), all of the military tank images in a data set are of the same model type, all of the satellite photos of a geographical region are cultivated, etc. As such, each data set may include observations representing families of patterns, where all patterns in a family share at least one characteristic. 
     Other variations of state may include without limitation physical conditions (e.g., temperature, pressure), positional conditions (e.g., distance, scale, translation, rotation), illumination (e.g., angle, intensity, frequency, wavelengths distributions) and other characteristics. It should also be understood that other variations of state may apply, particularly for patterns other than images (e.g., electroencephalography or “EEG” results, electrocardiogram or “EKG” results, audio signatures, etc.). A combination of values representing one or more select conditions of each observation (e.g., each image) defines the variation of state of that observation. 
     In addition, the pre-processor  202  also receives a data set  206  of multiple related but unlabeled images. The data set  206  represents multiple observations of a member of an unknown class. In one implementation, for example, each data set includes multiple images having variations of state distinguishing each image in the data set from each other image in the data set. 
     The pre-processor  202  estimates a point representing a given data set of patterns. An example mechanism for estimating a point (or any applicable point informed by variations in state) for a given individual person is described herein. However, it should be understood that such a mechanism or other similar mechanisms may be applied to other patterns (e.g., EEG results, EKG results, sonar signals, radar signals, microscopic patterns, satellite images, infrared images, ultraviolet images, etc.). In the case of faces, for example, each face image may be geometrically normalized based upon known eye positions. In addition, the background area outside the face itself (or some other predetermined sub-region) may be zeroed, erased, or ignored. However, a benefit of the described technology is that such normalization may be omitted or reduced in such as way as to take advantage of these variations. 
     In one implementation, a point on a parameter space is encoded by first mapping the set of images to a set of ordered orthonormal basis vectors (Stiefel manifold) or a subspace of a fixed vector space (Grassmann manifold). In another implementation, a point may be viewed as the concatenation of points arising from nested parameterizations of different dimensions. The set of orthonormal vectors may be found via a range of encoding algorithms including but not limited to the singular value decomposition (SVD), the generalized singular value decomposition, signal fraction analysis, principal vectors, independent component analysis and canonical correlation analysis. 
     As a generalization of such approaches, a set of data may be encoded to a point as a nested sequence of subspaces of fixed dimensions on a flag manifold. The encoding of a point on a parameter space may use the data in its original acquisition space or in an altered form. In one implementation, whether a point is encoded on a Grassmann, Stiefel, or flag manifold, or even a more general parameter space, is dependent upon the nature of the information comprising the data set. The Grassmann representation is coarser than the Stiefel representation because two points are identified if they are related by a rotational matrix (i.e., there is a subjective map from the Stiefel manifold to the Grassmann manifold by identifying an ordered k-tuple of orthonormal vectors to their span). The flag manifold point representation reveals information about the pattern set across a hierarchy of nested subspaces. Additionally, points on the flag manifold can have additional information attached to them by extending the flag manifold as a product manifold, which captures desirable weightings of the subspaces. 
     For example, each image may be unrolled into a vector x i   (j) , which is the j th  data set of observations or images of the subject i. Each data matrix X i  for data set of subject i (e.g., corresponding to an individual labeled array of patterns) is then denoted by X i =[X i   (1) | . . . |X i   (k) ], where there are k data sets of images of the subject i. This raw data matrix can, without data compression, be used to generate an ordered orthonormal basis, or sequence of nested bases, to encode the data set to a point or set of points on a nonlinear parameter space. 
     In some implementations, the data matrix may be initially reduced to yield the desired encoding representation on a nonlinear parameter space. For example, a subspace representation for the i th  subject may be constructed from the k images of its data matrix X i  via SVD. The q basis vectors for the i th  subject&#39;s q-dimensional subspace are the strongest q-left singular vectors in the SVD of X i . In other words, the q-dimensional subspace of X i  is given by the column space R(X i ) of its first q left singular vectors. 
     An orthogonal projection that is the transpose of the matrix of the left singular vectors obtained via SVD of X i  is applied to X i  to serve as a first-step dimensionality reduction. This computation allows selection of a parameter space upon which the data is encoded. 
     In some applications, one or more datasets may be augmented by including mirror images or additional images altered by some other transformation. For facial images, the symmetrization of the data set imposes even and odd symmetry on the basis functions analogous to sinusoidal expansions. For sets of facial images under varying illumination conditions, reflection augmentation may improve the estimated linear representation by both increasing the effective sample set and introducing novel illumination conditions. As a consequence, the estimation of points that capture variations in illumination for each class can be improved without acquiring additional data. 
     In addition to computing points on parameter spaces that capture the variation in illumination, the pre-processor  202  can also constrain the computation of points by restricting the raw data to one or more “patches” of the observation, to projections to fiducial points within the image, or even to an arbitrary selection of image points (e.g., random projections) within each image. In such implementations, the patches or data points used are consistent from observation to observation and data set to data set. (A patch may be considered a strictly or loosely connected set of points within the base image or pattern.) 
     A set of observations may also contain multiple modalities and within each modality a disparate number of points. For example, a set may consist of images each of which has a different number of pixels. The pre-processor  202  may normalize these images by interpolating values to make render each image the size of the highest resolution image. 
     Grassmann manifolds, as well as Stiefel manifolds and flag manifolds, allow for families of matrices that fit into the described framework, although other parameter spaces may be employed. A distinction between the Grassmann manifold and the Stiefel manifold is that the Grassmann manifold Gr(k,n) views every k-dimensional subspace of R n  (respectively C n ) as a point while a Stiefel manifold S(k,n) views every ordered k-tuple of orthonormal vectors in R n  (respectively C n ) as a point. The Stiefel manifolds are not rotationally invariant and afford discriminatory information when there is information in a subspace that is related to the ordering of the basis vectors. For example, different frequencies of illumination generate different bases. As such, if one does not want the subspace representation to be rotationally invariant, mapping the data sets to a Stiefel manifold may be preferred. In this choice, different orderings of the basis correspond to fundamentally different objects. In alternative implementations, flag manifolds are generalizations of a hierarchical subspace representation that creates a more refined structure for pattern recognition. Products of manifolds allow for even further refinement of the information that can be captured in a parameter space. 
     In summary, the pre-processor  202  estimates a point on a parameter space representing each data set of each class of facial images. This concept extends to other patterns and variations of state as well—for each pattern in a given family, a pre-processor estimates a point (e.g., a subspace, a sequence of nested subspaces or an ordered orthonormal basis, all representing a data set of patterns) in a geometric parameter space. Furthermore, it should be understood that the data from which each point is estimated may be constrained consistently across the patterns and data sets. For example, the estimation may be performed using only a region of each facial image corresponding to the right eye in each image. Alternatively, a pattern of arbitrarily-selected pixels may be extracted consistently from each image and the point estimate may be developed from the values of those arbitrarily-selected pixels. In this fashion, a point representing the X i   (•)  of each data set is generated. 
     A classifier  208  receives the computed points representing variations in illumination for both the data sets in the collection  204  and the data set  206 . The classified  208  applies the similarity criterion to yield the classification result  210  that identifies the class in which the data set  206  belongs or otherwise indicates that no classification was achieved. 
       FIG. 3  illustrates an example relationship defined between a labeled array  300  of data sets and an associated subset (as indicated by region  302 ) of a parameter space  304 . A mapping operator is generated from the training data of a labeled array such that each data set of the labeled array  300  can be mapped to a point on the parameter space  304 . For example, the left-most data set of labeled array  300  is mapped to a point on the parameter space  304 , as indicated by arrow  306 , and the right-most data set of labeled array  300  is mapped to a point on the parameter space  304 , as indicated by arrow  308 . Each point mapped from the data sets of the labeled array  300  onto the parameter space  304  is designated by an “x”. In aggregate, the mapped points would form a region (represented by line  310 ) on the parameter space  304 , which defines the subset  302  of the parameter space associated uniquely with the labeled array  300  and the Class  1 . 
       FIG. 4  illustrates an example relationship defined between another labeled array  400  of data sets and an associated subset (as indicated by region  402 ) of a parameter space, relative to a different subset  404  of the parameter space associated with a different array of data sets (not shown). A mapping operator is generated from the training data of a labeled array such that each data set of the labeled array  400  can be mapped to a point on the parameter space  406 . For example, the left-most data set of labeled array  400  is mapped to a point on the parameter space  406 , as indicated by arrow  408 , and the right-most data set of labeled array  400  is mapped to a point on the parameter space  406 , as indicated by arrow  410 . Each point mapped from the data sets of the labeled array  400  and of the different array of data sets (not shown) onto the parameter space  406  is designated by an “x”. 
     In aggregate, the mapped points of the labeled array  400  form a region on the parameter space  406 , which defines a subset  404  of the parameter space associated uniquely with the labeled array  400  and the Class N. The subsets defined for each labeled array by the mapping(s) of data sets to points on the parameter space  406  do not tend to overlap in the parameter space  406 , although overlapping may occur in some circumstances. As such, a data set of unlabeled elements can be mapped to the parameter space  406  using the mapping operator of each class and can then be similarity tested to determine if it lies on or substantially on one of the class subsets of the parameter space  406 . 
     In one implementation, similarity measurements (e.g., d(•)) are executed on the data set of unlabeled elements using each of the mapping operators i, where i=1, . . . , N. The minimum similarity measurement indicates the subset on the parameter space  406  on which the point is best located—a similarity criterion. (In addition, as part of a similarity criterion, a similarity threshold may also be applied to a minimum similarity measurement that is not small enough to warrant classification in the class associated with the mapping operator.) As such, the classifier designates the data set of unlabeled elements as being a member of a class if (a) the similarity measurement associated with that class is the minimum across all classes; and (b) the similarity measurement satisfies a designated threshold. Condition (a) picks the class having the best fit for the data set of unlabeled elements. Condition (b) indicates that no pattern match results if the similarity measurement is too large (e.g., if the training data of the labeled arrays does not include images of the individual in the unknown set). If no pattern match results, then the classifier may indicate that a new class has been detected. 
     Mathematical support for the described approach is provided in the context of classifying video sequences based on a set of N classes ω 1 , ω 2 , ω 3  . . . ω N . The derivation below is given in terms of Grassmann Manifolds, but the approach may be applied to other parameters spaces, as previously discussed. Accordingly, training data in the form of data sets in labeled arrays is collected such that the data sets of each labeled array map to points on a Grassmannian (i.e., on Gr(k,n). For example, the training data for class ω i  maps to points
 
 D   i   ={X   i   (1)   , . . . , X   i   (P     i     ) }
 
which is a set of points on Gr(k,n), or in terms of raw data, a set of data sets of data points. The notation Pi represents the number of images in each data set. In this manner, the data sets associated with a particular class can be viewed as a cloud of points on the Grassmannian (see, e.g., regions  402  and  404  of  FIG. 4 ). The domain values X as well as the range values M(X) reside on the Grassmannian Gr(k,n).
 
     For each class i, a mapping operator may be defined as M (i) : Gr(k,n)→Gr(k,n), such that the mapping operator M (i)  takes the form
 
 M ( X )= D ·( D   T   *D ) −1   D   T *( X )
 
where the * operator represents a nonlinear similarity measure. An example nonlinear * operator is constructed as a function of the principal angles between the data in D and the point X ∈ Gr(k,n), such as
 
               a   *   b     =     1   -       (     2   π     )     ⁢       θ   r     ⁡     (     a   ,   b     )                 
where θ r (a,b) is the r th  principal angle between the vector spaces V a , V b  associated to the points a,b ∈ Gr(k,n). Another example nonlinear * operator may be given by
 
               a   *   b     =         ∑   i     ⁢           ⁢     θ   i   2               
although other operators may also be employed.
 
     For each ω i  and every collected labeled array X ∈ ω i , a mapping M (i)  is constructed such that the similarity criterion of Equation 1 is satisfied,
 
 d ( X,M   (i) ( X ))&lt;ε M   (1)
 
where d(•) represents a similarity measurement defined on the appropriate parameter space. The requirements on the magnitude of the intrinsic modeling error value ε M  are flexible and may be taken to be based on factors such as the machine precision, reasonable lnposelstarterrorlnposelends expected in nonlinear optimization problems (e.g., ε M =O(10 −4 )). A similarity criterion may also be based on a different error value, such as one present for a given data matrix.
 
     An example similarity measurement may be computed as distance between two points X,Y ∈ Gr(k,n) in the form, although other similarity measurements may be employed: 
     
       
         
           
             
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     Success of the classifier is derived from the property that the action of M (i)  for points of unknown data sets near the training data for class i is similar to the action of the same M (i)  for actual training data points. For example, a high quality classifier satisfies the property
 
 d ( {tilde over (X)},M   (i) ({tilde over ( X )}))&lt;κ i ε M  
 
where d(X,{tilde over (X)})&lt;δ and constant error threshold κ i ε M  (a defined threshold) represents a measure of the quality of the identity mapping M (i) .
 
     For all i≠j and {tilde over (X)} ∈ ω i , then the following relationship ship is designed:
 
 d ( {tilde over (X)},M   (j) ({tilde over ( X )}))&gt;&gt;κ i ε M  
 
     Accordingly, a data set Y of unlabeled patterns may be classified by solving the classification criterion: 
     
       
         
           
             
               
                 
                   
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     Equation 2 essentially sweeps through all of the class mapping operators M (i)  to identify the particular M (i)  that behaves most like the identity for the point mapped from data set Y. In Equation 2, the classification criterion is a minimum similarity measurement across all of the classes, although other criteria may be applied. The data set Y is then assigned to the identified class ω i . In some implementations, this assignment is dependent upon the value of d(X,M (i) (X)) not exceeding a similarity threshold. 
     Note: It is also reasonable to construct a collection of mappings {M j   (i) } j=1   K     i    of the identity for the data associated with each class i and to combine their contributions in a similar fashion. Each mapping for a class, while derived from the labeled array&#39;s data sets, can be defined over different domains and ranges. 
       FIG. 5  illustrates example operations  500  for performing set-to-set pattern recognition. A receiving operation  502  receives a collection of N labeled arrays of data sets of patterns, each data set including related patterns (e.g., images of the same subject) having variations of state and each labeled array containing related data sets. The data sets of these labeled arrays are encoded on the parameter space (e.g., a nonlinear parameter space). A mapping operation  504  generates a mapping operator for each class that maps each training data set of corresponding labeled array to at least one point on a parameter space. In one implementation, a mapping operator
 
 M   i ( X   i )= D   i ·( D   i   T   *D   i ) −1   D   i   T *( X   i )
 
is used to map the data sets D i ={X i   (1) , . . . , X i   (P     i     ) } of a labeled array i on the parameter space. Each mapping of the data sets of a labeled array defines a subset of the parameter space.
 
     Another receiving operation  506  receives a data set of related unlabeled patterns (e.g., images having the same subject), wherein the patterns exhibit variations of state (e.g., illumination, pose, attire, facial hair, hair style, etc.). 
     A similarity test operation  508  tests the data set of related unlabeled patterns against the mapping of the data set of related unlabeled patterns to the parameter space, using the mapping operator of a current class. A result of the similarity test operation  508  is a similarity measurement relating to the current class and the data set of related unlabeled patterns. 
     An iteration operation  510  determines whether any additional classes are available to be tested. If so, an incrementing operation  515  increments the processing loop to the next available class. If not, a selection operation  512  determines whether a class has been found that results in the minimum similarity measurement and satisfies a similarity threshold. If so, a classification operation  514  assigns the data set of related unlabeled patterns to the class of the mapping operator that satisfied the similarity criterion of selection operation  512 . If not, the data set of related unlabeled patterns is deemed unclassifiable or is assigned to a new class. The data set may then be added to the training data under its newly identified classification. 
     Note: It should be understood that implementations may be made that omit the similarity threshold from the similarity criterion. Likewise, other similarity criterion may also be used to determine which mapping operator best maps the data set of the related unlabeled patterns to a labeled subset of the parameter space. 
       FIG. 6  illustrates components of an example system that can be useful in the implementation of the described technology. A general purpose computer system  600  is capable of executing a computer program product to execute a computer process. Data and program files may be input to the computer system  600 , which reads the files and executes the programs therein. Some of the elements of a general purpose computer system  600  are shown in  FIG. 6  wherein a processor  602  is shown having an input/output (I/O) section  604 , a Central Processing Unit (CPU)  606 , and a memory section  608 . There may be one or more processors  602 , such that the processor  602  of the computer system  600  comprises a single central-processing unit  606 , or a plurality of processing units, commonly referred to as a parallel processing environment. The computer system  600  may be a conventional computer, a distributed computer, or any other type of computer. The described technology is optionally implemented in software devices loaded in memory  608 , stored on a configured DVD/CD-ROM  610  or storage unit  612 , and/or communicated via a wired or wireless network link  614  on a carrier signal, thereby transforming the computer system  600  in  FIG. 6  to a special purpose machine for implementing the described operations. 
     The I/O section  604  is connected to one or more user-interface devices (e.g., a keyboard  616  and a display unit  618 ), a disk storage unit  612 , and a disk drive unit  620 . Generally, in contemporary systems, the disk drive unit  620  is a DVD/CD-ROM drive unit capable of reading the DVD/CD-ROM medium  610 , which typically contains programs and data  622 . Computer program products containing mechanisms to effectuate the systems and methods in accordance with the described technology may reside in the memory section  604 , on a disk storage unit  612 , or on the DVD/CD-ROM medium  610  of such a system  600 . Alternatively, a disk drive unit  620  may be replaced or supplemented by a floppy drive unit, a tape drive unit, or other storage medium drive unit. The network adapter  624  is capable of connecting the computer system to a network via the network link  614 , through which the computer system can receive instructions and data embodied in a carrier wave. Examples of such systems include but are not limited to personal computers offered manufacturers of Intel-compatible computing systems, PowerPC-based computing systems, ARM-based computing systems, and other systems running a UNIX-based or other operating system. It should be understood that computing systems may also embody devices such as Personal Digital Assistants (PDAs), mobile phones, gaming consoles, set top boxes, etc. 
     When used in a LAN-networking environment, the computer system  600  is connected (by wired connection or wirelessly) to a local network through the network interface or adapter  624 , which is one type of communications device. When used in a WAN-networking environment, the computer system  600  typically includes a modem, a network adapter, or any other type of communications device for establishing communications over the wide area network. In a networked environment, program modules depicted relative to the computer system  600  or portions thereof, may be stored in a remote memory storage device. It is appreciated that the network connections shown are exemplary and other means of and communications devices for establishing a communications link between the computers may be used. 
     In an example implementation, a pre-processor, a classifier module, and other modules may be embodied by instructions stored in memory  608  and/or storage devices  612  or storage media  610  and processed by the processing unit  606 . Labeled arrays of data sets, unlabeled data sets, mapped data, and other data may be stored in memory  608  and/or storage devices  612  or storage media  610  as persistent datastores. 
     The technology described herein is implemented as logical operations and/or modules in one or more systems. The logical operations may be implemented as a sequence of processor-implemented steps executing in one or more computer systems and as interconnected machine or circuit modules within one or more computer systems. Likewise, the descriptions of various component modules may be provided in terms of operations executed or effected by the modules. The resulting implementation is a matter of choice, dependent on the performance requirements of the underlying system implementing the described technology. Accordingly, the logical operations making up the embodiments of the technology described herein are referred to variously as operations, steps, objects, or modules. Furthermore, it should be understood that logical operations may be performed in any order, unless explicitly claimed otherwise or a specific order is inherently necessitated by the claim language. 
     The above specification, examples and data provide a complete description of the structure and use of example embodiments of the invention. Although various embodiments of the invention have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. In particular, it should be understood that the described technology may be employed independent of a personal computer. Other embodiments are therefore contemplated. It is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative only of particular embodiments and not limiting. Changes in detail or structure may be made without departing from the basic elements of the invention as defined in the following claims. 
     Although the subject matter has been described in language specific to structural features and/or methodological arts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts descried above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claimed subject matter.