Wang, Liping; Chen, Songcan; Wang, Yuanping - In: Computational Optimization and Applications 58 (2014) 2, pp. 409-421
Recently, matrix norm <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$l_{2,1}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>l</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </math> </EquationSource> </InlineEquation> has been widely applied to feature selection in many areas such as computer vision, pattern recognition, biological study and etc. As an extension of <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$$l_1$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>l</mi> <mn>1</mn> </msub> </math> </EquationSource> </InlineEquation> norm, <InlineEquation ID="IEq6"> <EquationSource Format="TEX">$$l_{2,1}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>l</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </math> </EquationSource> </InlineEquation> matrix norm is often used to find...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>