This abstract proposes to automatically estimate the optimal regularization parameter of SRDA based on the perturbation linear discriminant analysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed. One is derived from the method of optimal regularization parameter estimation for SRDA. The other is to utilize the kernel […]
HOW TO ESTIMATE THE REGULARIZATION PARAMETER FOR SRDA AND ITS KERNEL VERSION
Dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction. Spectral regression discriminant analysis (SRDA) has recently been proposed as an efficient solution to large-scale subspace learning problems. There is a tunable regularization parameter in SRDA, which is critical to algorithm […]
DBP WITH THE APPLICATION IN HOTEL ENERGY MANAGEMENT
For DBP, optimization problem is formulated with the objective of maximizing expected reward, which is received when the amount of energy saving satisfies the contract. For a general distribution of energy consumption, we give a general condition for the optimal bid and outline an algorithm to find the solution without numerical integration. Furthermore, for Gaussian […]
HOTEL ENERGY MANAGEMENT USING DEMAND BIDDING PROGRAM AND ITS APPLICATION
Demand bidding program (DBP) is one type of demand response. DBP attracts large energy consumers to participate and encourages them to reduce their energy use by setting their own target. The customer is free to choose a bidding value in terms of the amount of energy reduction. If the actual amount of energy saving conforms […]
A NOVEL ITERATIVE TRICLASS THRESHOLDING TECHNIQUE BASED ON OTSU’S METHOD IN IMAGE SEGMENTATION
Image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries […]
DISTRIBUTED DATA WITH INCREMENTAL DETECTION OF INCONSISTENCIES
Errors in the data are typically detected as violations of constraints (data quality rules), such as functional dependencies (FDs), denial constraints, and conditional functional dependencies (CFDs). When the data is in a centralized database, it is known that two SQL queries suffice to detect its violations of a set of CFDs. This abstract investigates incremental […]
IMAGE SEGMENTATION USING ITERATIVE TRICLASS THRESHOLDING TECHNIQUE
In image processing, segmentation is often the first step to pre-process images to extract objects of interest for further analysis. We present a new method in image segmentation that is based on Otsu’s method but iteratively searches for sub regions of the image for segmentation, instead of treating the full image as a whole region […]
INCREMENTAL DETECTION IN DISTRIBUTED DATA
Real life data is often dirty. To clean the data, efficient algorithms for detecting errors have to be in place. Errors in the data are typically detected as violations of constraints (data quality rules), such as functional dependencies (FDs), denial constraints, and conditional functional dependencies (CFDs). When the data is in a centralized database, it […]
SALIENCY DRIVEN NONLINEAR DIFFUSION FILTERING BASED IMAGE CLASSIFICATION USING MULTISCALE INFORMATION FUSION
Image classification is perhaps the most important part of digital image analysis. It is very nice to have a “pretty picture” or an image, showing a magnitude of colors illustrating various features of the underlying terrain, but it is quite useless unless to know what the colors mean. The image is classified using multiscale information […]
SALIENCY DRIVEN IMAGE MULTISCALE NONLINEAR DIFFUSION FILTERING
The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford […]