Singular vector decomposition

Bases are the central idea of linear algebra. An invertable square matrix has eigenvectors. A symetric matrix has orthogonal eigenvectors with non-negative eigenvalues, i.e. positive semidefinite. A matrix has two types of singular vectors, left and right signular vectors, $$A=U\Sigma V^{T}$$.

When we think the matrix $$A$$ is data points of rows $$A=U\Sigma V^{T}$$ like data table, The right singular vectors $$V$$ build bases, the sigular values $$\Sigma$$ are magnitude of the bases and the left singular values $$U$$ becomes new data points on new bases. The new data points $$U$$ are orthonormal.

When we think the matrix $$A$$ is a system of linear transformation $$Ax=b,\ U\Sigma V^{T}x=b$$, a vector $$x$$ is repositioned on right singular vector coordinates $$V$$ then the coordinates are multiplied by $$\Sigma$$ and finally linear transformed by left singular vector $$U$$.

A matrix is sum of rank one singular matrix. $A = \sigma_{1} u_{1}u_{1}^{T} + \cdots + \sigma_{k} u_{k}u_{k}^{T}$ The Eckart-Young theorem finds closest low-rank matrix $$A_k$$.
In symetric matrix, the bases (right singular vectors) and it’s value on the bases (left singular vectors) are same. Reproducing kernel hilbert space has same values on it’s base functions.

Rayleigh quotient $R(x) = {{x^{T}Sx} }$ has maximum $$\lambda_{1}$$ at the eigen vector $$q_{1}$$ and saddle points at $$x=q_{k},\ \frac{\partial R}{\partial x_{i}} = 0$$. The second eigenvector can be found by Lagrangian optimization problum maximizing $$\ R(x)$$ s.t. $$q_{1} = 0$$.

Pseudoinversion $$A^{+}$$ process does first inversing value with $$U^{T}$$, and scale with $$\Sigma ^{+}$$ and followed by reversing axis $$V^{T}$$.

When $$Ax=b$$ has many solutions, minimizing $$\lVert A \rVert$$ s.t. $$Ax=b$$ can be best solution. The $$L_{1}$$ norm has sparse solution. Jun Kang
Clinical Assistant Professor of Hospital Pathology

My research interests include pathology, oncology and statistics.