Multiview Hessian regularized logistic regression for action recognition
نویسندگان
چکیده
منابع مشابه
Multiview Hessian regularized logistic regression for action recognition
With the rapid development of social media sharing, people often need to manage the growing volume of multimedia data such as large scale video classification and annotation, especially to organize those videos containing human activities. Recently, manifold regularized semi-supervised learning (SSL), which explores the intrinsic data probability distribution and then improves the generalizatio...
متن کاملL1-regularized Logistic Regression Stacking and Transductive CRF Smoothing for Action Recognition in Video
For the 2013 THUMOS challenge we built a bag-offeatures pipeline based on a variety of features extracted from both video and keyframe modalities. In addition to the quantized, hard-assigned features provided by the organizers, we extracted local HOG and Motion Boundary Histogram (MBH) descriptors aligned with dense trajectories in video to capture motion. We encode them as Fisher vectors. To r...
متن کاملSimulation-based Regularized Logistic Regression
In this paper, we develop a simulation-based framework for regularized logistic regression, exploiting two novel results for scale mixtures of normals. By carefully choosing a hierarchical model for the likelihood by one type of mixture, and implementing regularization with another, we obtain new MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the d...
متن کاملEfficient L1 Regularized Logistic Regression
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many pract...
متن کاملDistributed Newton Method for Regularized Logistic Regression
Regularized logistic regression is a very successful classification method, but for large-scale data, its distributed training has not been investigated much. In this work, we propose a distributed Newton method for training logistic regression. Many interesting techniques are discussed for reducing the communication cost. Experiments show that the proposed method is faster than state of the ar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal Processing
سال: 2015
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2014.08.002