Based on the floating reference theory, a new method for extracting the net analyte signal (NAS) is proposed. The noise background subspace is spanned by spectra at the floating radial reference point, and then, the spectra at the measurement point a
Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of low-rank tensor factorization (LRTF) has been applied for the recovery of a low dimensional subspace from high dimensional visual data. H
Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of tensor factorization has been applied for the recovery of a low dimensional subspace from high dimensional visual data. Generally, the re
The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and th
To transfer a calibration model in the case where only the master and slave spectra of standardization.samples are available, principal component analysis (PCA) and kernel principal component analysis.(KPCA) based joint spectral space (termed as JPCA
Recently, subspace clustering has achieved promising clustering quality by performing spectral clustering over an affinity graph. It is a key to construct a robust affinity matrix in graph-oriented subspace clustering. Sparse representation can repre
A new subspace learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the objective function of locality preserving projections (LPP). LPDP retains the
