14. An apparatus having a central processing unit (CPU) and a memory coupled to said CPU comprising: training logic configured to train a probabilistic latent semantic analysis (PLSA) model, the training logic further configured to apply an expectation maximization algorithm utilizing a set of expectation maximization equations corresponding to a PLSA model based on using P(z|w); term identification logic configured to identify at least one new term w from a document d to be added to said trained PLSA model; term addition logic configured to incrementally add said at least one new term to said trained PLSA model, the term addition logic further configured to apply said expectation maximization algorithm utilizing only a subset comprising at least one of said expectation maximization equations, wherein parameters dependent on the new term w and the document d are used by the term addition logic, the set of expectation maximization equations comprising: P ⁡ ( z ❘ d , w ) = P ⁡ ( z ❘ d ) ⁢ P ⁡ ( z ❘ w ) ⁢ / ⁢ P ⁡ ( z ) ∑ z ′ ⁢ ⁢ P ⁡ ( z ′ ❘ d ) ⁢ P ⁡ ( z ′ ❘ w ) ⁢ / ⁢ P ⁢ { z ′ ) ; P ⁡ ( z ❘ w ) = ∑ d ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; P ⁡ ( z ❘ d ) = ∑ w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ w , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; ⁢ and P ⁡ ( z ) = ⁢ ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) , wherein P(z) represents a probability of a latent class z, P(z|d) represents a probability of a latent class z given a document d, P(z|w) represents a probability of a latent class z given a term w, and f(d,w) represents the number of times the term w occurs in the document d, and wherein the term addition logic is further configured to keep track of a total count N when incrementally adding said at least one new term, wherein: N adj = N old + ∑ d new ∈ D new ⁢ ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ; P adj ⁡ ( z ) = N old ⁢ P old ⁡ ( z ) + ∑ d new ∈ D new ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ⁢ P ⁡ ( z ❘ d new , w ) N adj ; and wherein N adj represents an adjusted value of N, N old represents the previous value of N, d new represents the new document d, D new represents a collection of new documents D, W old represents a previous set of terms W, W new represents a new set of terms which replaces W, P old (z) represents the previous value of P(z), and P adj (z) represents a new value of P(z) which replaces the previous value of P(z); and presentation logic configured to present a model parameter from said trained PLSA model, the presented model parameter including at least one of an updated P(z|d) value and an updated P(z|w) value.
14. An apparatus having a central processing unit (CPU) and a memory coupled to said CPU comprising: training logic configured to train a probabilistic latent semantic analysis (PLSA) model, the training logic further configured to apply an expectation maximization algorithm utilizing a set of expectation maximization equations corresponding to a PLSA model based on using P(z|w); term identification logic configured to identify at least one new term w from a document d to be added to said trained PLSA model; term addition logic configured to incrementally add said at least one new term to said trained PLSA model, the term addition logic further configured to apply said expectation maximization algorithm utilizing only a subset comprising at least one of said expectation maximization equations, wherein parameters dependent on the new term w and the document d are used by the term addition logic, the set of expectation maximization equations comprising: P ⁡ ( z ❘ d , w ) = P ⁡ ( z ❘ d ) ⁢ P ⁡ ( z ❘ w ) ⁢ / ⁢ P ⁡ ( z ) ∑ z ′ ⁢ ⁢ P ⁡ ( z ′ ❘ d ) ⁢ P ⁡ ( z ′ ❘ w ) ⁢ / ⁢ P ⁢ { z ′ ) ; P ⁡ ( z ❘ w ) = ∑ d ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; P ⁡ ( z ❘ d ) = ∑ w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ w , z ′ ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ′ ❘ d , w ) ; ⁢ and P ⁡ ( z ) = ⁢ ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) ⁢ P ⁡ ( z ❘ d , w ) ∑ d , w ⁢ ⁢ f ⁡ ( d , w ) , wherein P(z) represents a probability of a latent class z, P(z|d) represents a probability of a latent class z given a document d, P(z|w) represents a probability of a latent class z given a term w, and f(d,w) represents the number of times the term w occurs in the document d, and wherein the term addition logic is further configured to keep track of a total count N when incrementally adding said at least one new term, wherein: N adj = N old + ∑ d new ∈ D new ⁢ ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ; P adj ⁡ ( z ) = N old ⁢ P old ⁡ ( z ) + ∑ d new ∈ D new ⁢ ∑ w ∈ W old ⋃ W new ⁢ ⁢ f ⁡ ( d new , w ) ⁢ P ⁡ ( z ❘ d new , w ) N adj ; and wherein N adj represents an adjusted value of N, N old represents the previous value of N, d new represents the new document d, D new represents a collection of new documents D, W old represents a previous set of terms W, W new represents a new set of terms which replaces W, P old (z) represents the previous value of P(z), and P adj (z) represents a new value of P(z) which replaces the previous value of P(z); and presentation logic configured to present a model parameter from said trained PLSA model, the presented model parameter including at least one of an updated P(z|d) value and an updated P(z|w) value. 17. The apparatus of claim 14 , wherein the term addition logic includes update logic configured to update at least one P(z|d) parameter.