This　paper　presents　a　new　algorithm　based　on　the　theory　of　mutual　information　and　information　geometry.　This　algorithm　places　emphasis　on　adaptive　mutual　information　estimation　and　maximum　likelihood　estimation.　With　the　theory　of　information　geometry,　we　adjust　the　mutual　information　along　the　geodesic　line.　Finally,　we　evaluate　our　proposal　using　empirical　datasets　that　are　dedicated　for　classification　and　regression.　The　results　show　that　our　algorithm　contributes　to　a　significant　improvement　over　existing　methods.　©　2019　by　the　authors.