Dimensionality-reduction using connectionist networks
نویسندگان
چکیده
منابع مشابه
Dimensionality Reduction Using Neural Networks
A multi-layer neural network with multiple hidden layers was trained as an autoencoder using steepest descent, scaled conjugate gradient and alopex algorithms. These algorithms were used in different combinations with steepest descent and alopex used as pretraining algorithms followed by training using scaled conjugate gradient. All the algorithms were also used to train the autoencoders withou...
متن کاملComputational modeling of dynamic decision making using connectionist networks
In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decisio...
متن کاملImage Reduction Using Assorted Dimensionality Reduction Techniques
Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-T...
متن کاملDimensionality Reduction using Relative Attributes
Visual attributes are high-level semantic description of visual data that are close to the language of human. They have been intensively used in various applications such as image classification [1,2], active learning [3,4], and interactive search [5]. However, the usage of attributes in dimensionality reduction has not been considered yet. In this work, we propose to utilize relative attribute...
متن کاملDimensionality Reduction using GA-PSO
The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. In this paper, we propose a combination of genetic algorithms (GAs) and particle swarm optimization (PSO) for feature selection. The K-nearest nei...
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: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 1989
ISSN: 0162-8828
DOI: 10.1109/34.21799