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Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI
Relevance generation and 2.
Streaming Kernel Principal Component Analysis
Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning.
Frequent Directions : Simple and Deterministic Matrix Sketching
It performed $O(d \times \ell)$ operations per row and maintains a sketch matrix $B \in R^{\ell \times d}$ such that for any $k < \ell$ $\|A^TA - B^TB \|_2 \leq \|A - A_k\|_F^2 / (\ell-k)$ and $\|A - \pi_{B_k}(A)\|_F^2 \leq \big(1 + \frac{k}{\ell-k}\big) \|A-A_k\|_F^2 $ .
Data Structures and Algorithms 68W40 (Primary)
