Rotational Invariant Dimensionality Reduction Algorithms
A common intrinsic limitation of the traditional subspace learning methods is the sensitivity to the outliers and the image variations of the object since they use the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2}} $ </tex-math></inline-formula> norm as the metric. In this paper, a series of methods based on the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2,1}} $ </tex-math></inline-formula>-norm are proposed for linear dimensionality reduction. Since the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2,1}} $ </tex-math></inline-formula>-norm based objective function is robust to the image variations, the proposed algorithms can perform robust image feature extraction for classification. We use different ideas to design different algorithms and obtain a unified rotational invariant (RI) dimensionality reduction framework, which extends the well-known graph embedding algorithm framework to a more generalized form. We provide the comprehensive analyses to show the essential properties of the proposed algorithm framework. This paper indicates that the optimization problems have global optimal solutions when all the orthogonal projections of the data space are computed and used. Experimental results on popular image datasets indicate that the proposed RI dimensionality reduction algorithms can obtain competitive performance compared with the previous <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {L_{2}}$ </tex-math></inline-formula> norm based subspace learning algorithms.