Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too large if we want to deploy them on memory-limited devices such as mobile phones, smart watches and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data which can preserve the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with binary coefficients which can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be -1, 0 or 1 during the training stage and coefficient 0 can be removed during model inference. Moreover, we provide detailed analysis on the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM model with memory cost less than 30KB.

Subsampled Randomized Hadamard Transform (SRHT), a popular random projection method that can efficiently project a d-dimensional data into r-dimensional space (r ≪ d) in O(dlog(d)) time, has been widely used to address the challenge of high-dimensionality in machine learning. SRHT works by rotating the input data matrix X ∈ ℝn × d by Randomized Walsh-Hadamard Transform followed with a subsequent uniform column sampling on the rotated matrix. Despite the advantages of SRHT, one limitation of SRHT is that it generates the new low-dimensional embedding without considering any specific properties of a given dataset. Therefore, this data-independent random projection method may result in inferior and unstable performance when used for a particular machine learning task, e.g., classification. To overcome this limitation, we analyze the effect of using SRHT for random projection in the context of linear SVM classification. Based on our analysis, we propose importance sampling and deterministic top-r sampling to produce effective low-dimensional embedding instead of uniform sampling SRHT. In addition, we also proposed a new supervised non-uniform sampling method. Our experimental results have demonstrated that our proposed methods can achieve higher classification accuracies than SRHT and other random projection methods on six real-life datasets.

To make every user conveniently have access to his or her most interested jobs and candidates (recommendation items) in the current employment market, the recruitment networks need to meet the demand of fast and accurate recommendation. But a key challenge is that the total items may have a large quantity in the big data scenarios. And another problem is that the personalization of different users is diverse. In order to handle these challenges, this paper proposes a mining and prediction system for job and candidate recommendation with contextual online learning. It predicts a proper item by utilizing the feedback reward of previous users in the nearby context region. Besides that, we introduce a Monte-Carlo Tree Search (MCTS) method in which the similar items can be amalgamated into a cluster to reduce the computing load. Our algorithm can achieve sublinear regret and space complexity. Finally, some experiments are conducted to test our algorithm based on a large database from \emph{Work4} (the global leader in social and mobile recruiting), which can show the outstanding performance of our algorithm when compared with other existing algorithms.