Sparse Sequential Dirichlet Coding
نویسندگان
چکیده
This short paper describes a simple coding technique, Sparse Sequential Dirichlet Coding, for multi-alphabet memoryless sources. It is appropriate in situations where only a small, unknown subset of the possible alphabet symbols can be expected to occur in any particular data sequence. We provide a competitive analysis which shows that the performance of Sparse Sequential Dirichlet Coding will be close to that of a Sequential Dirichlet Coder that knows in advance the exact subset of occurring alphabet symbols. Empirically we show that our technique can perform similarly to the more computationally demanding Sequential Sub-Alphabet Estimator, while using less computational resources.
منابع مشابه
Face Recognition using an Affine Sparse Coding approach
Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...
متن کاملA hierarchical latent topic model based on sparse coding
We propose a novel hierarchical latent topic model based on sparse coding in this paper. Unlike the other topic models applied in the computer vision field, the words in our model are not discrete but continuous. They are generated by sparse coding and represented with n-dimensional vectors in R. In sparse coding, only a small set of components of each word is active, so we assume the probabili...
متن کاملTraffic Scene Analysis using Hierarchical Sparse Topical Coding
Analyzing motion patterns in traffic videos can be exploited directly to generate high-level descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this pa...
متن کاملRice Classification and Quality Detection Based on Sparse Coding Technique
Classification of various rice types and determination of its quality is a major issue in the scientific and commercial fields associated with modern agriculture. In recent years, various image processing techniques are used to identify different types of agricultural products. There are also various color and texture-based features in order to achieve the desired results in this area. In this ...
متن کاملMarkov Latent Feature Models
We introduce Markov latent feature models (MLFM), a sparse latent feature model that arises naturally from a simple sequential construction. The key idea is to interpret each state of a sequential process as corresponding to a latent feature, and the set of states visited between two null-state visits as picking out features for an observation. We show that, given some natural constraints, we c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1206.3618 شماره
صفحات -
تاریخ انتشار 2012