An efficient kernel matrix evaluation measure
نویسندگان
چکیده
Article history: Received 16 October 2007 Accepted 3 April 2008
منابع مشابه
Kernel Matrix Evaluation
We study the problem of evaluating the goodness of a kernel matrix for a classification task. As kernel matrix evaluation is usually used in other expensive procedures like feature and model selections, the goodness measure must be calculated efficiently. Most previous approaches are not efficient, except for Kernel Target Alignment (KTA) that can be calculated in O(n) time complexity. Although...
متن کاملEmbed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
The kernel k-means is an effective method for data clustering which extends the commonly-used k-means algorithm to work on a similarity matrix over complex data structures. It is, however, computationally very complex as it requires the complete kernel matrix to be calculated and stored. Further, its kernelized nature hinders the parallelization of its computations on modern scalable infrastruc...
متن کاملAn infeasible interior-point method for the $P*$-matrix linear complementarity problem based on a trigonometric kernel function with full-Newton step
An infeasible interior-point algorithm for solving the$P_*$-matrix linear complementarity problem based on a kernelfunction with trigonometric barrier term is analyzed. Each (main)iteration of the algorithm consists of a feasibility step andseveral centrality steps, whose feasibility step is induced by atrigonometric kernel function. The complexity result coincides withthe best result for infea...
متن کاملAn efficient kernel product for automatic differentiation libraries, with applications to measure transport
This paper presents a memory-efficient implementation of the kernel matrix-vector product (sparse convolution) and the way to link it with automatic differentiation libraries such as PyTorch. This piece of software alleviates the major bottleneck of autodiff libraries as far as diffeomorphic shape registration is concerned: memory consumption. As a result, symbolic python code can now scale up ...
متن کاملRandom Binary Mappings for Kernel Learning and Efficient SVM
We propose to learn the kernel of an SVM as the weighted sum of a large number of simple, randomized binary stumps. Each stump takes one of the extracted features as input. This leads to an efficient and very fast SVM, while also alleviating the task of kernel selection. We demonstrate the capabilities of our kernel on 6 standard vision benchmarks, in which we combine several common image descr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Pattern Recognition
دوره 41 شماره
صفحات -
تاریخ انتشار 2008