Task-driven Greedy Learning of Feature Hashing Functions
نویسندگان
چکیده
Randomly hashing multiple features into one aggregated feature is routinely used in largescale machine learning tasks to both increase speed and decrease memory requirements, with little or no sacrifice in performance. In this paper we investigate whether using a learned (instead of a random) hashing function improves performance. We show experimentally that with increasing difference between the dimensionalities of the input space and the hashed space, learning hashes is increasingly useful compared to random hashing.
منابع مشابه
Image authentication using LBP-based perceptual image hashing
Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for percep...
متن کاملLearning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational co...
متن کاملMultiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval
Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing ...
متن کاملA Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories: feature learning approach, low-rank approach, task clustering approach,...
متن کاملBiocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
We describe our approach for the SemEval-2014 task 9: Sentiment Analysis in Twitter. We make use of an ensemble learning method for sentiment classification of tweets that relies on varied features such as feature hashing, part-of-speech, and lexical features. Our system was evaluated in the Twitter message-level task.
متن کامل