Sparse preserving feature weights learning
نویسندگان
چکیده
منابع مشابه
Locality Preserving Feature Learning
Locality Preserving Indexing (LPI) has been quite successful in tackling document analysis problems, such as clustering or classification. The approach relies on the Locality Preserving Criterion, which preserves the locality of the data points. However, LPI takes every word in a data corpus into account, even though many words may not be useful for document clustering. To overcome this problem...
متن کاملLearning Feature Weights from Positive Cases
The availability of new data sources presents both opportunities and challenges for the use of Case-based Reasoning to solve novel problems. In this paper, we describe the research challenges we faced when trying to reuse experiences of successful academic collaborations available online in descriptions of funded grant proposals. The goal is to recommend the characteristics of two collaborators...
متن کاملLearning Feature Weights for Similarity Measures
When employing a similarity function to measure the similarity between two cases, one large problem is how to determine the feature weights. This paper presents a new method for learning feature weights in a similarity function from the given similarity information. The similarity information can be divided into two kinds: One is called qualitative similarity information which represents the si...
متن کاملOn Random Weights and Unsupervised Feature Learning
Recently two anomalous results in the literature have shown that certain feature learning architectures can yield useful features for object recognition tasks even with untrained, random weights. In this paper we pose the question: why do random weights sometimes do so well? Our answer is that certain convolutional pooling architectures can be inherently frequency selective and translation inva...
متن کاملLearning Feature Weights from Case Order Feedback
Defining adequate similarity measures is one of the most difficult tasks when developing CBR applications. Unfortunately, only a limited number of techniques for supporting this task by using machine learning techniques have been developed up to now. In this paper, a new framework for learning similarity measures is presented. The main advantage of this approach is its generality, because its a...
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: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.12.020