2 Classification of One-Dimensional Data 2 2.1 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Building the LDA Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Results of One-Dimensional LDA Classification . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Quadratic Discriminant Analysis . . . . . ....

Dimensionality reduction (DR) of data is a crucial issue for many machine learning tasks, such as pattern recognition and classification. In this paper, we present quantum algorithm circuit to efficiently perform linear discriminant analysis (LDA) dimensionality reduction. Firstly, the presented improves existing LDA avoid error caused by irreversibility between-class scatter matrix $S_B$ in or...

However, there are strong connections between these approaches when we deal with a binary target attribute. In this particular case, we can even recreate the outputs of the linear discriminant analysis with a linear regression program (Bishop, 2007, pages 189 190; Duda et al., 2001, pages 242 – 243; Huberty et Olejnik, 2006, pages 353 – 355; Nakache et Confais, 2003, pages 14 – 16; Saporta, 200...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Multi-label problems arise frequently in image and video annotations, and many other related applications such as multi-topic text categorization, music classification, etc. Like other computer vision tasks, multi-label image and video annotations also suffer from the difficulty of high dimensionality because images often have a large number of features. Linear discriminant analysis (LDA) is a ...

Standard probabilistic linear discriminant analysis (PLDA) for speaker recognition assumes that the sample’s features (usually, i-vectors) are given by a sum of three terms: a term that depends on the speaker identity, a term that models the within-speaker variability and is assumed independent across samples, and a final term that models any remaining variability and is also independent across...

This paper discusses the method of setting up the financial early warning model of listed companies based on the researches before. With the data from the public financial information report of listed companies we choose the appropriate financial indicators. Then Based on the data of 29 ST listing companies and 128 similar normal ones, we give our factor analysis model and discriminant analysis...

In this paper, we propose a novel online multi-camera framework for person identification based on gait recognition using Grassmann Discriminant Analysis. We propose an online method wherein the gait space of individuals are created as they are tracked. The gait space is view invariant and the recognition process is carried out in a distributed manner. We assume that only a fixed known set of p...