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I have been working on latent semantic analysis lately. I have implemented it in java by making use of the Jama package. Here is the code: Matrix vtranspose ; a = new Matrix(termdoc); termdoc = a.getArray(); a = a.transpose() ; SingularValueDecomposition sv =new SingularValueDecomposition(a) ; u = sv.getU(); v = sv.getV(); s = sv.getS(); vtranspose = v.transpose() ; // we obtain this as a result of svd uarray = u.getArray(); sarray = s.getArray(); varray = vtranspose.getArray(); if(semantics.maketerms.nodoc>50) { sarray_mod = new double[50][50]; uarray_mod = new double[uarray.length][50]; varray_mod = new double[50][varray.length]; move(sarray,50,50,sarray_mod); move(uarray,uarray.length,50,uarray_mod); move(varray,50,varray.length,varray_mod); e = new Matrix(uarray_mod); f = new Matrix(sarray_mod); g = new Matrix(varray_mod); Matrix temp =e.times(f); result = temp.times(g); } else { Matrix temp = u.times(s); result = temp.times(vtranspose); } result = result.transpose(); results = result.getArray() ; return results ; But how do we determine the number of dimensions? Is there a method to determine the number of dimensions to which the system should be reduced to obtain best results? What other parameters do we consider for effective performance of LSA?

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