Sparse Discriminant Analysis

A Note On the Connection and Equivalence of Three Sparse Linear Discriminant Analysis Methods

In this paper we reveal the connection and equivalence of three sparse linear discriminant analysis methods: the `1-Fisher’s discriminant analysis proposed in Wu et al. (2008), the sparse optimal scoring proposed in Clemmensen et al. (2011) and the direct sparse discriminant analysis proposed in Mai et al. (2012). It is shown that, for any sequence of penalization parameters, the normalized sol...

A direct approach to sparse discriminant analysis in ultra-high dimensions

Sparse discriminant methods based on independence rules, such as the nearest shrunken centroids classifier (Tibshirani et al., 2002) and features annealed independence rules (Fan & Fan, 2008), have been proposed as computationally attractive tools for feature selection and classification with high-dimensional data. A fundamental drawback of these rules is that they ignore correlations among fea...

Mapping natural habitats using remote sensing and Sparse partial least square discriminant analysis

An Efficient Approach to Sparse Linear Discriminant Analysis

We present a novel approach to the formulation and the resolution of sparse Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal Scoring. It has an exact equivalence with penalized LDA, contrary to the multi-class approaches based on the regression of class indicator that have been proposed so far. Sparsity is obtained thanks to a group-Lasso penalty that selects the ...

Aggregating Private Sparse Learning Models Using Multi-Party Computation

We consider the problem of privately learning a sparse model across multiple sensitive datasets, and propose learning individual models locally and privately aggregating them using secure multi-party computation. In this paper, we report some preliminary experiments on distributed sparse linear discriminant analysis, showing both the feasibility and effectiveness of our approach on experiments ...

Sparse Local Discriminant Projections for Face Feature Extraction

One of the major disadvantages of the linear dimensionality reduction algorithms, such as Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are that the projections are linear combination of all the original features or variables and all weights in the linear combination known as loadings are typically non-zero. Thus, they lack physical interpretation in many applicatio...