The Sparse Multinomial Logistic Regression (SMLR) method introduced in (Krishnapuram, 2005) is among the state-of-the-art in supervised learning. However its application to large datasets, such as hyperspectral imagery is still a rather challenging task from the computational point of view, sometimes even impossible to perform. In this paper, the Bregman iteration-based SMLR method (Bregman-SML...

Nonobese diabetic (NOD) is an inbred mouse strain susceptible to development of T cell-mediated autoimmune diabetes. The strain is characterized by high percentages of T lymphocytes in lymphoid organs. The syngeneic mixed lymphocyte reaction (SMLR), a T cell response to self MHC class II Ag, is reportedly involved in the generation of a number of immunoregulatory cells, including suppressor ind...

When a wavelet to be estimated is not spiky, then a single most likely replacement (SMLR) detector, which is used to detect randomly located impulsive events that have Gaussian-distributed amplitudes, may split a large spike into two smaller ones and may also detect some spikes at wrong locations, although these locations are very close to their true ones. Presented here are two new detection a...

This research was conducted as a preliminary step toward developing a real-time spectral-based nitrogen sensor for citrus trees. Diffuse reflectance of leaf samples, with five nitrogen application rates (0, 112, 168, 224, and 280 kg ha-1), was measured from 400 to 2500 nm using a spectrophotometer in a laboratory environment. A correlation coefficient spectrum, a stepwise multiple linear regres...

Abstract: We propose the sparse multinomial logistic regression (SMLR) model for spectral-spatial classification of hyperspectral images. In the proposed method, the parameters of SMLR are iteratively estimated from logposterior by using Laplace approximation. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The ...

The Sparse Multinomial Logistic Regression (SMLR) method introduced in (Krishnapuram, 2005) is among the state-of-the-art in supervised learning. However its application to large datasets, such as hyperspectral imagery is still a rather challenging task from the computational point of view, sometimes even impossible to perform. In this paper, the Bregman iteration-based SMLR method (Bregman-SML...

In the past decade, many detection and estimation algorithms have been reported for estimating a desired Bernoulli-Gaussian signal which was distorted by a linear time-invariant system. The well known Kormylo and Mendel’s single most likely replacement (SMLR) algorithm, which works well and has been successfully used to process real seismic data, is an oflline signal processing algorithm. The p...

Assessing vegetation water content (VWC) from hyperspectral reflectance dataset poses two foremost questions: what specific wavebands of the SWIR offer a good retrieval and what modeling methods have the best predictive ability. In this paper, we explored the application of multivariate statistical techniques such as stepwise multiple linear regression (SMLR) and partial least square regression...

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation...

We have studied the proliferative response of unprimed T cells to syngeneic dendritic cells (DC) (syngeneic mixed leukocyte reaction [SMLR]) in cultures of mouse spleen and lymph node. T cells purified by passage over nylon wool contain few DC and exhibits little proliferative activity during several days of culture. Addition of small numbers of purified syngeneic DC induces substantial, dose-d...